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Journal articles on the topic 'User Preference Models'
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1
Savia, Eerika, Kai Puolamäki, and Samuel Kaski. "Latent grouping models for user preference prediction." Machine Learning 74, no. 1 (September 3, 2008): 75–109. http://dx.doi.org/10.1007/s10994-008-5081-7.
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Gautschi, David A., and Darius J. Sabavala. "Incorporating user costs in preference models for service alternatives." Marketing Letters 2, no. 3 (August 1991): 281–91. http://dx.doi.org/10.1007/bf02404078.
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Zhou, Yinglian, and Jifeng Chen. "Time Series Geographic Social Network Dynamic Preference Group Query." International Journal of Information Systems in the Service Sector 13, no. 4 (October 2021): 18–39. http://dx.doi.org/10.4018/ijisss.2021100102.
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Driven by experience and social impact of the new life, user preferences continue to change over time. In order to make up for the shortcomings of existing geographic social network models that often cannot obtain user dynamic preferences, a time-series geographic social network model was constructed to detect user dynamic preferences, a dynamic preference value model was built for user dynamic preference evaluation, and a dynamic preferences group query (DPG) was proposed in this paper . In order to optimize the efficiency of the DPG query algorithm, the UTC-tree index user timing check-in record is designed. UTC-tree avoids traversing all user check-in records in the query, accelerating user dynamic preference evaluation. Finally, the DPG query algorithm is used to implement a well-interacted DPG query system. Through a large number of comparative experiments, the validity of UTC-tree and the scalability of DPG query are verified.
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Chen, Xu, Yongfeng Zhang, and Zheng Qin. "Dynamic Explainable Recommendation Based on Neural Attentive Models." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 53–60. http://dx.doi.org/10.1609/aaai.v33i01.330153.
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Providing explanations in a recommender system is getting more and more attention in both industry and research communities. Most existing explainable recommender models regard user preferences as invariant to generate static explanations. However, in real scenarios, a user’s preference is always dynamic, and she may be interested in different product features at different states. The mismatching between the explanation and user preference may degrade costumers’ satisfaction, confidence and trust for the recommender system.
With the desire to fill up this gap, in this paper, we build a novel Dynamic Explainable Recommender (called DER) for more accurate user modeling and explanations. In specific, we design a time-aware gated recurrent unit (GRU) to model user dynamic preferences, and profile an item by its review information based on sentence-level convolutional neural network (CNN). By attentively learning the important review information according to the user current state, we are not only able to improve the recommendation performance, but also can provide explanations tailored for the users’ current preferences. We conduct extensive experiments to demonstrate the superiority of our model for improving recommendation performance. And to evaluate the explainability of our model, we first present examples to provide intuitive analysis on the highlighted review information, and then crowd-sourcing based evaluations are conducted to quantitatively verify our model’s superiority.
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Wang, Jenq-Haur, Yen-Tsang Wu, and Long Wang. "Predicting Implicit User Preferences with Multimodal Feature Fusion for Similar User Recommendation in Social Media." Applied Sciences 11, no. 3 (January 25, 2021): 1064. http://dx.doi.org/10.3390/app11031064.
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In social networks, users can easily share information and express their opinions. Given the huge amount of data posted by many users, it is difficult to search for relevant information. In addition to individual posts, it would be useful if we can recommend groups of people with similar interests. Past studies on user preference learning focused on single-modal features such as review contents or demographic information of users. However, such information is usually not easy to obtain in most social media without explicit user feedback. In this paper, we propose a multimodal feature fusion approach to implicit user preference prediction which combines text and image features from user posts for recommending similar users in social media. First, we use the convolutional neural network (CNN) and TextCNN models to extract image and text features, respectively. Then, these features are combined using early and late fusion methods as a representation of user preferences. Lastly, a list of users with the most similar preferences are recommended. The experimental results on real-world Instagram data show that the best performance can be achieved when we apply late fusion of individual classification results for images and texts, with the best average top-k accuracy of 0.491. This validates the effectiveness of utilizing deep learning methods for fusing multimodal features to represent social user preferences. Further investigation is needed to verify the performance in different types of social media.
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Yang, Nihong, Lei Chen, and Yuyu Yuan. "An Improved Collaborative Filtering Recommendation Algorithm Based on Retroactive Inhibition Theory." Applied Sciences 11, no. 2 (January 18, 2021): 843. http://dx.doi.org/10.3390/app11020843.
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Collaborative filtering (CF) is the most classical and widely used recommendation algorithm, which is mainly used to predict user preferences by mining the user’s historical data. CF algorithms can be divided into two main categories: user-based CF and item-based CF, which recommend items based on rating information from similar user profiles (user-based) or recommend items based on the similarity between items (item-based). However, since user’s preferences are not static, it is vital to take into account the changing preferences of users when making recommendations to achieve more accurate recommendations. In recent years, there have been studies using memory as a factor to measure changes in preference and exploring the retention of preference based on the relationship between the forgetting mechanism and time. Nevertheless, according to the theory of memory inhibition, the main factors that cause forgetting are retroactive inhibition and proactive inhibition, not mere evolutions over time. Therefore, our work proposed a method that combines the theory of retroactive inhibition and the traditional item-based CF algorithm (namely, RICF) to accurately explore the evolution of user preferences. Meanwhile, embedding training is introduced to represent the features better and alleviate the problem of data sparsity, and then the item embeddings are clustered to represent the preference points to measure the preference inhibition between different items. Moreover, we conducted experiments on real-world datasets to demonstrate the practicability of the proposed RICF. The experiments show that the RICF algorithm performs better and is more interpretable than the traditional item-based collaborative filtering algorithm, as well as the state-of-art sequential models such as LSTM and GRU.
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Wu, Ping, Tao Yu, J. B. Du, G. Q. Qu, and Feng Xiong. "Research on Modeling User’s Preference in the Steel E-Trading Platform." Applied Mechanics and Materials 743 (March 2015): 687–91. http://dx.doi.org/10.4028/www.scientific.net/amm.743.687.
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In order to meet the increasing personalized needs of users in the steel trading platform, the intelligent recommendation system has been introduced into the platform. And the users’ interests and preferences-based modeling is the key and foundation of recommendation system, and changes with the change of time. So, in this paper, the user preferences are divided into long-term and short-term firstly, then the users’ basic information vectors and cluster method are used to model users’ long-term interests and preferences, while mining and analyzing users’ operating records in the platform to model users’ the short-term. Finally, the whole interest and preference’s model of user will be built by integrating the two models.
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Jin, Tao, Pan Xu, Quanquan Gu, and Farzad Farnoud. "Rank Aggregation via Heterogeneous Thurstone Preference Models." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4353–60. http://dx.doi.org/10.1609/aaai.v34i04.5860.
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We propose the Heterogeneous Thurstone Model (HTM) for aggregating ranked data, which can take the accuracy levels of different users into account. By allowing different noise distributions, the proposed HTM model maintains the generality of Thurstone's original framework, and as such, also extends the Bradley-Terry-Luce (BTL) model for pairwise comparisons to heterogeneous populations of users. Under this framework, we also propose a rank aggregation algorithm based on alternating gradient descent to estimate the underlying item scores and accuracy levels of different users simultaneously from noisy pairwise comparisons. We theoretically prove that the proposed algorithm converges linearly up to a statistical error which matches that of the state-of-the-art method for the single-user BTL model. We evaluate the proposed HTM model and algorithm on both synthetic and real data, demonstrating that it outperforms existing methods.
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Liu, Qinghua, Marta Crispino, Ida Scheel, Valeria Vitelli, and Arnoldo Frigessi. "Model-Based Learning from Preference Data." Annual Review of Statistics and Its Application 6, no. 1 (March 7, 2019): 329–54. http://dx.doi.org/10.1146/annurev-statistics-031017-100213.
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Preference data occur when assessors express comparative opinions about a set of items, by rating, ranking, pair comparing, liking, or clicking. The purpose of preference learning is to ( a) infer on the shared consensus preference of a group of users, sometimes called rank aggregation, or ( b) estimate for each user her individual ranking of the items, when the user indicates only incomplete preferences; the latter is an important part of recommender systems. We provide an overview of probabilistic approaches to preference learning, including the Mallows, Plackett–Luce, and Bradley–Terry models and collaborative filtering, and some of their variations. We illustrate, compare, and discuss the use of these methods by means of an experiment in which assessors rank potatoes, and with a simulation. The purpose of this article is not to recommend the use of one best method but to present a palette of different possibilities for different questions and different types of data.
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Xu, Xiao, Fang Dong, Yanghua Li, Shaojian He, and Xin Li. "Contextual-Bandit Based Personalized Recommendation with Time-Varying User Interests." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 6518–25. http://dx.doi.org/10.1609/aaai.v34i04.6125.
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A contextual bandit problem is studied in a highly non-stationary environment, which is ubiquitous in various recommender systems due to the time-varying interests of users. Two models with disjoint and hybrid payoffs are considered to characterize the phenomenon that users' preferences towards different items vary differently over time. In the disjoint payoff model, the reward of playing an arm is determined by an arm-specific preference vector, which is piecewise-stationary with asynchronous and distinct changes across different arms. An efficient learning algorithm that is adaptive to abrupt reward changes is proposed and theoretical regret analysis is provided to show that a sublinear scaling of regret in the time length T is achieved. The algorithm is further extended to a more general setting with hybrid payoffs where the reward of playing an arm is determined by both an arm-specific preference vector and a joint coefficient vector shared by all arms. Empirical experiments are conducted on real-world datasets to verify the advantages of the proposed learning algorithms against baseline ones in both settings.
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Cheng, Weiyu, Yanyan Shen, Linpeng Huang, and Yanmin Zhu. "Dual-Embedding based Deep Latent Factor Models for Recommendation." ACM Transactions on Knowledge Discovery from Data 15, no. 5 (June 26, 2021): 1–24. http://dx.doi.org/10.1145/3447395.
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Among various recommendation methods, latent factor models are usually considered to be state-of-the-art techniques, which aim to learn user and item embeddings for predicting user-item preferences. When applying latent factor models to the recommendation with implicit feedback, the quality of embeddings always suffers from inadequate positive feedback and noisy negative feedback. Inspired by the idea of NSVD that represents users based on their interacted items, this article proposes a dual-embedding based deep latent factor method for recommendation with implicit feedback. In addition to learning a primitive embedding for a user (resp. item), we represent each user (resp. item) with an additional embedding from the perspective of the interacted items (resp. users) and propose attentive neural methods to discriminate the importance of interacted users/items for dual-embedding learning. We design two dual-embedding based deep latent factor models, DELF and DESEQ, for pure collaborative filtering and temporal collaborative filtering (i.e., sequential recommendation), respectively. The novel attempt of the proposed models is to capture each user-item interaction with four deep representations that are subtly fused for preference prediction. We conducted extensive experiments on four real-world datasets. The results verify the effectiveness of user/item dual embeddings and the superior performance of our methods on item recommendation.
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TRABELSI, WALID, NIC WILSON, DEREK BRIDGE, and FRANCESCO RICCI. "PREFERENCE DOMINANCE REASONING FOR CONVERSATIONAL RECOMMENDER SYSTEMS: A COMPARISON BETWEEN A COMPARATIVE PREFERENCES AND A SUM OF WEIGHTS APPROACH." International Journal on Artificial Intelligence Tools 20, no. 04 (August 2011): 591–616. http://dx.doi.org/10.1142/s021821301100036x.
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A conversational recommender system iteratively shows a small set of options for its user to choose between. In order to select these options, the system may analyze the queries tried by the user to derive whether one option is dominated by others with respect to the user's preferences. The system can then suggest that the user try one of the undominated options, as they represent the best options in the light of the user preferences elicited so far. This paper describes a framework for preference dominance. Two instances of the framework are developed for query suggestion in a conversational recommender system. The first instance of the framework is based on a basic quantitative preferences formalism, where options are compared using sums of weights of their features. The second is a qualitative preference formalism, using a language that generalises CP-nets, where models are a kind of generalised lexicographic order. A key feature of both methods is that deductions of preference dominance can be made efficiently, since this procedure needs to be applied for many pairs of options. We show that, by allowing the recommender to focus on undominated options, which are ones that the user is likely to be contemplating, both approaches can dramatically reduce the amount of advice the recommender needs to give to a user compared to what would be given by systems without this kind of reasoning.
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Keppens, J., and Q. Shen. "Compositional Model Repositories via Dynamic Constraint Satisfaction with Order-of-Magnitude Preferences." Journal of Artificial Intelligence Research 21 (April 1, 2004): 499–550. http://dx.doi.org/10.1613/jair.1335.
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The predominant knowledge-based approach to automated model construction, compositional modelling, employs a set of models of particular functional components. Its inference mechanism takes a scenario describing the constituent interacting components of a system and translates it into a useful mathematical model. This paper presents a novel compositional modelling approach aimed at building model repositories. It furthers the field in two respects. Firstly, it expands the application domain of compositional modelling to systems that can not be easily described in terms of interacting functional components, such as ecological systems. Secondly, it enables the incorporation of user preferences into the model selection process. These features are achieved by casting the compositional modelling problem as an activity-based dynamic preference constraint satisfaction problem, where the dynamic constraints describe the restrictions imposed over the composition of partial models and the preferences correspond to those of the user of the automated modeller. In addition, the preference levels are represented through the use of symbolic values that differ in orders of magnitude.
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Zhang, Wei, Yue Ying, Pan Lu, and Hongyuan Zha. "Learning Long- and Short-Term User Literal-Preference with Multimodal Hierarchical Transformer Network for Personalized Image Caption." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 9571–78. http://dx.doi.org/10.1609/aaai.v34i05.6503.
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Personalized image caption, a natural extension of the standard image caption task, requires to generate brief image descriptions tailored for users' writing style and traits, and is more practical to meet users' real demands. Only a few recent studies shed light on this crucial task and learn static user representations to capture their long-term literal-preference. However, it is insufficient to achieve satisfactory performance due to the intrinsic existence of not only long-term user literal-preference, but also short-term literal-preference which is associated with users' recent states. To bridge this gap, we develop a novel multimodal hierarchical transformer network (MHTN) for personalized image caption in this paper. It learns short-term user literal-preference based on users' recent captions through a short-term user encoder at the low level. And at the high level, the multimodal encoder integrates target image representations with short-term literal-preference, as well as long-term literal-preference learned from user IDs. These two encoders enjoy the advantages of the powerful transformer networks. Extensive experiments on two real datasets show the effectiveness of considering two types of user literal-preference simultaneously and better performance over the state-of-the-art models.
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Roh, Hyuk-Jae. "Mode Choice Behavior of Various Airport User Groups for Ground Airport Access." Open Transportation Journal 7, no. 1 (October 18, 2013): 43–55. http://dx.doi.org/10.2174/1874447820130930002.
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In this research, we used a multinomial logit (MNL) discrete choice analysis technique to deepen the understanding of the mode choice behavior of various airport user groups categorized by trip purpose and trip distance for ground airport access. We used revealed preference (RP) data collected by an on-site-survey administrated by the Korea Transport Institute (KOTI) at the Kimpo International Airport passenger terminal in South Korea. Initially, four basic models were selected from a variety of model specifications, and these were analyzed to address general preferences in mode choice. The models were then evaluated in terms of the resulting estimation. The best-fitting model specification among four models was chosen for further study. Both trip distance models (standard-distance (SD) and long-distance (LD)) and trip purpose models (departing (D) and non-departing (ND)) were estimated. The results analyzed in this study encompass an unambiguous spectrum of mode choice behaviors associated with distinct airport user groups. The fundamental information, either revealed or reflected by modeling ground airport access for various airport user groups, could be essential not only to transportation planners -especially at the first phase of airport planning- but also to airport authorities faced with difficulties in managing ground transportation facilities to effectively serve airport users.
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Clark, Calvin, Patricia Mokhtarian, Giovanni Circella, and Kari Watkins. "User Preferences for Bicycle Infrastructure in Communities with Emerging Cycling Cultures." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 12 (June 27, 2019): 89–102. http://dx.doi.org/10.1177/0361198119854084.
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Non-motorized travel modes, particularly cycling, are experiencing a resurgence in many United States (U.S.) states as well as in other countries. Still, most studies focus on bicyclists’ behaviors in areas with strong bicycling cultures. This paper discusses the findings of a survey (N = 1,178) deployed in six communities in Alabama and Tennessee, U.S., where cycling is not (yet) popular nor widely adopted. The analysis includes linear regression models built on respondents’ reactions to images of bicycling infrastructure and their perceptions of being comfortable, safe, and willing to try cycling on the displayed roadway type. Findings indicate a preference for more separated bicycle infrastructure types along with options that exclude on-street parking. Segmented models indicate that, compared with potential cyclists, the preferences of regular utilitarian cyclists can vary more than those of recreational/occasional cyclists. Results from this study provide useful insights into ways to maximize the return on investments, and design bike infrastructure that can attract patronage and be most successful in areas lacking a substantial bicycling population.
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Zhu, Zhigang, Jin Chen, Lei Zhang, Yaohua Chang, Tyler Franklin, Hao Tang, and Arber Ruci. "iASSIST." International Journal of Multimedia Data Engineering and Management 11, no. 4 (October 2020): 38–59. http://dx.doi.org/10.4018/ijmdem.2020100103.
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The iASSIST is an iPhone-based assistive sensor solution for independent and safe travel for people who are blind or visually impaired, or those who simply face challenges in navigating an unfamiliar indoor environment. The solution integrates information of Bluetooth beacons, data connectivity, visual models, and user preferences. Hybrid models of interiors are created in a modeling stage with these multimodal data, collected, and mapped to the floor plan as the modeler walks through the building. Client-server architecture allows scaling to large areas by lazy-loading models according to beacon signals and/or adjacent region proximity. During the navigation stage, a user with the navigation app is localized within the floor plan, using visual, connectivity, and user preference data, along an optimal route to their destination. User interfaces for both modeling and navigation use multimedia channels, including visual, audio, and haptic feedback for targeted users. The design of human subject test experiments is also described, in addition to some preliminary experimental results.
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Manouselis, Nikos, and Andreas M. Maras. "Multi-attribute Services Brokering in Agent-based Virtual Private Networks." Computing Letters 1, no. 3 (March 6, 2005): 137–43. http://dx.doi.org/10.1163/1574040054861230.
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This paper presents the development of an agent-based Virtual Private Network (VPN) system that supports multimedia service brokering. The VPN agents employ multi-attribute preference models in order to represent the end-user preferences, and a multi-criteria decision making model to evaluate available services from network providers. A prototype multi-agent system demonstrating the proposed approach has also been implemented.
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Wang, Can, Tao Bo, Yun Wei Zhao, Chi-Hung Chi, Kwok-Yan Lam, Sen Wang, and Min Shu. "Behavior-Interior-Aware User Preference Analysis Based on Social Networks." Complexity 2018 (October 9, 2018): 1–18. http://dx.doi.org/10.1155/2018/7371209.
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There is a growing trend recently in big data analysis that focuses on behavior interiors, which concern the semantic meanings (e.g., sentiment, controversy, and other state-dependent factors) in explaining the human behaviors from psychology, sociology, cognitive science, and so on, rather than the data per se as in the case of exterior dimensions. It is more intuitive and much easier to understand human behaviors with less redundancy in concept by exploring the behavior interior dimensions, compared with directly using behavior exteriors. However, they usually approach from a unidimensional perspective with a lack of a sense of interrelatedness. Thus, integrating multiple behavior dimensions together into some numerical measures to form a more comprehensive view for subsequent prediction processes becomes a pivotal issue. Moreover, these studies usually focus on the magnitude but neglect the associated temporal features. In this paper, we propose a behavior interior dimension-based neighborhood collaborative filtering method for the top-N hashtag adoption frequency prediction that takes into account the interdependence in temporal dynamics. Our proposed approach couples the similarity in user preference and their impact propagation, by integrating the linear threshold model and the enhanced CF model based on behavior interiors. Experiments on Twitter demonstrate that the behavior-interior-aware CF models achieve better adoption prediction results than the state-of-the-art methods, and the joint consideration of similarity in user preference and their impact propagation results in a significant improvement than treating them separately.
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Jin, Yingjie, and Chunyan Han. "A music recommendation algorithm based on clustering and latent factor model." MATEC Web of Conferences 309 (2020): 03009. http://dx.doi.org/10.1051/matecconf/202030903009.
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The collaborative filtering recommendation algorithm is a technique for predicting items that a user may be interested in based on user history preferences. In the recommendation process of music data, it is often difficult to score music and the display score data for music is less, resulting in data sparseness. Meanwhile, implicit feedback data is more widely distributed than display score data, and relatively easy to collect, but implicit feedback data training efficiency is relatively low, usually lacking negative feedback. In order to effectively solve the above problems, we propose a music recommendation algorithm combining clustering and latent factor models. First, the user-music play record data is processed to generate a user-music matrix. The data is then analyzed using a latent factor probability model on the resulting matrix to obtain a user preference matrix U and a musical feature matrix V. On this basis, we use two K- means algorithms to perform user clustering and music clustering on two matrices. Finally, for the user preference matrix and the commodity feature matrix that complete the clustering, a user-based collaborative filtering algorithm is used for prediction. The experimental results show that the algorithm can reduce the running cost of large-scale data and improve the recommendation effect.
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Luo, Liangchen, Wenhao Huang, Qi Zeng, Zaiqing Nie, and Xu Sun. "Learning Personalized End-to-End Goal-Oriented Dialog." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6794–801. http://dx.doi.org/10.1609/aaai.v33i01.33016794.
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Most existing works on dialog systems only consider conversation content while neglecting the personality of the user the bot is interacting with, which begets several unsolved issues. In this paper, we present a personalized end-to-end model in an attempt to leverage personalization in goal-oriented dialogs. We first introduce a PROFILE MODEL which encodes user profiles into distributed embeddings and refers to conversation history from other similar users. Then a PREFERENCE MODEL captures user preferences over knowledge base entities to handle the ambiguity in user requests. The two models are combined into the PERSONALIZED MEMN2N. Experiments show that the proposed model achieves qualitative performance improvements over state-of-the-art methods. As for human evaluation, it also outperforms other approaches in terms of task completion rate and user satisfaction.
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Qomariyah, Nunung Nurul, Dimitar Kazakov, and Ahmad Nurul Fajar. "On the benefit of logic-based machine learning to learn pairwise comparisons." Bulletin of Electrical Engineering and Informatics 9, no. 6 (December 1, 2020): 2637–49. http://dx.doi.org/10.11591/eei.v9i6.2384.
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In recent years, many daily processes such as internet web searching, e-mail filter-ing, social media services, e-commerce have benefited from machine learning tech-niques (ML). The implementation of ML techniques has been largely focused on blackbox methods where the general conclusions are not easily interpretable. Hence, theelaboration with other declarative software models to identify the correctness and com-pleteness of the models is not easy to perform. On the other hand, the emerge of somelogic-based machine learning techniques with their advantage of white box approachhave been proven to be well-suited for many software engineering tasks. In this paper,we propose the use of a logic-based approach to learn user preference in the form ofpairwise comparisons. APARELL as a novel approach of inductive learning is able tomodel the user’s preferences in description logic representation. This offers a rich, re-lational representation which is then can be used to produce a set of recommendations.A user study has been performed in our experiment to evaluate the implementation ofpairwise preference recommender system when compared to a standard list interface.The result of the experiment shows that the pairwise interface was significantly betterthan the other interface in many ways.
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Li, Hongzhi, and Dezhi Han. "A Novel Time-Aware Hybrid Recommendation Scheme Combining User Feedback and Collaborative Filtering." Mobile Information Systems 2020 (October 22, 2020): 1–16. http://dx.doi.org/10.1155/2020/8896694.
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Nowadays, recommender systems are used widely in various fields to solve the problem of information overload. Collaborative filtering and content-based models are representative solutions in recommender systems; however, the content-based model has some shortcomings, such as single kind of recommendation results and lack of effective perception of user preferences, while for the collaborative filtering model, there is a cold start problem, and such a model is greatly affected by its adopted clustering algorithm. To address these issues, a hybrid recommendation scheme is proposed in this paper, which is based on both collaborative filtering and content-based. In this scheme, we propose the concept of time impact factor, and a time-aware user preference model is built based on it. Also, user feedback on recommendation items is utilized to improve the accuracy of our proposed recommendation model. Finally, the proposed hybrid model combines the results of content recommendation and collaborative filtering based on the logistic regression algorithm.
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Kharchevnikova, A. S., and A. V. Savchenko. "Visual preferences prediction for a photo gallery based on image captioning methods." Computer Optics 44, no. 4 (August 2020): 618–26. http://dx.doi.org/10.18287/2412-6179-co-678.
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The paper considers a problem of extracting user preferences based on their photo gallery. We propose a novel approach based on image captioning, i.e., automatic generation of textual descriptions of photos, and their classification. Known image captioning methods based on convolutional and recurrent (Long short-term memory) neural networks are analyzed. We train several models that combine the visual features of a photograph and the outputs of an Long short-term memory block by using Google's Conceptual Captions dataset. We examine application of natural language processing algorithms to transform obtained textual annotations into user preferences. Experimental studies are carried out using Microsoft COCO Captions, Flickr8k and a specially collected dataset reflecting the user’s interests. It is demonstrated that the best quality of preference prediction is achieved using keyword search methods and text summarization from Watson API, which are 8 % more accurate compared to traditional latent Dirichlet allocation. Moreover, descriptions generated by trained neural models are classified 1 – 7 % more accurately when compared to known image captioning models.
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GUAN, SHENG-UEI, WEN PIN TAN, and FEI LIU. "COGBROKER — A COGNITIVE APPROACH TO INTELLIGENT PRODUCT BROKERING FOR E-COMMERCE." International Journal of Computational Intelligence and Applications 07, no. 04 (December 2008): 401–27. http://dx.doi.org/10.1142/s1469026808002363.
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Researchers have proposed intelligent product-brokering applications to help facilitate the m-commerce shopping process. However, most algorithms require explicit, user-provided feedback to learn about user preference. In practical applications, users may not be motivated to provide unrewarded and time-consuming feedback. By adopting a cognitive approach, this paper investigates the possibility of replacing user feedback with user behavioral data analysis during product browsing. By means of evolutionary algorithms, the system is able to derive corresponding models that simulate the user's shopping behavior. User group profiling is also implemented to help identify the user's shopping patterns. Upon simulations of trial cases with consistent and rational shopping patterns, our experimental results confirm this approach being promising. The system shows high accuracy in detecting the preferences of the user. The algorithms are also portable and effective across different products.
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Tomeo, Paolo, Ignacio Fernández-Tobías, Iván Cantador, and Tommaso Di Noia. "Addressing the Cold Start with Positive-Only Feedback Through Semantic-Based Recommendations." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 25, Suppl. 2 (December 2017): 57–78. http://dx.doi.org/10.1142/s0218488517400116.
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Recommender systems aim to provide users with accurate item suggestions in a personalized fashion, but struggle in the case of cold start users, for whom there is a scarcity of preference data. User preferences can be either explicitly stated by the users — often by means of ratings —, or implicitly acquired by a system — for instance by mining text reviews, search queries, and purchase records. Recommendation methods have been mostly designed to deal with numerical ratings. However, real scenarios with user preferences expressed in the form of binary and unary (positive-only) feedback, e.g. the thumbs up/down in YouTube, and the likes in Facebook, are increasingly popular, and make the user cold start problem even more challenging. To address the cold start with positive-only feedback situations, we propose to exploit data additional to user preferences by means of specialized hybrid recommendation methods. In particular, we investigate a number of graph-based and matrix factorization recommendation models that jointly exploit user preferences and item semantic metadata automatically extracted from the well-known knowledge graph of DBpedia. Following a rigorous evaluation methodology for cold start, we empirically compare the above hybrid recommendation models on a Facebook dataset containing users likes for items in three different domains, namely books, movies and music. The achieved experimental results show that the semantics-aware hybrid approaches we propose outperform content-based and collaborative filtering baselines. In addition to recommendation accuracy, in our evaluation we also consider individual and aggregate diversity of recommendations as key quality factors in the users’ satisfaction.
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Zinas, Bako Zachariah, and Mahmud Mohd Jusan. "Choice Behaviour of Housing Attributes: Theory and measurement." Asian Journal of Environment-Behaviour Studies 2, no. 2 (January 1, 2017): 23–37. http://dx.doi.org/10.21834/aje-bs.v2i2.175.
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Preferences and choices in a society are constant dynamic operations, made based on the behavioural dynamism of people. In this cosmic dynamism, they keep shifting from one stage to another, within the same cosmic space. Housing preferences and choices, like any other life interests, therefore operate within this framework. Unlike merchandised products brands, housing brands are hardly known, probably because of the heterogeneous nature of the housing product - the house. However, very little is known about the relevant housing attributes (refer to page 7). Housing preferences and choices operate within the framework of preferences and choices for housing attributes. In any preference and choice activity, there are underlying motivations that make it possible for an individual to choose from available alternatives within a given product field. This paper examines and outlines the methodological and theoretical framework of housing preferences and choices, based on the theory of means-end chain (MEC). Previous MEC applications in the field of architecture and urban design have been very useful and successful. The paper attempts to explore from literature the possibility of extending the previous methods and their applicability in design process. In dealing with user preference for housing, there is a need for research for a development of a technological tool to identify user needs and preference, and the kind of decision support that is required to identify these needs.
Keywords: housing preference and choice, means-end chain, laddering technique, models.
© 2017 The Authors. Published for AMER ABRA by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, UniversitiTeknologi MARA, Malaysia.
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Fay, Damien, Liam O’Toole, and Kenneth N. Brown. "Gaussian Process models for ubiquitous user comfort preference sampling; global priors, active sampling and outlier rejection." Pervasive and Mobile Computing 39 (August 2017): 135–58. http://dx.doi.org/10.1016/j.pmcj.2016.08.012.
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Xia, Hongbin, Yang Luo, and Yuan Liu. "Attention neural collaboration filtering based on GRU for recommender systems." Complex & Intelligent Systems 7, no. 3 (January 30, 2021): 1367–79. http://dx.doi.org/10.1007/s40747-021-00274-4.
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AbstractThe collaborative filtering method is widely used in the traditional recommendation system. The collaborative filtering method based on matrix factorization treats the user’s preference for the item as a linear combination of the user and the item latent vectors, and cannot learn a deeper feature representation. In addition, the cold start and data sparsity remain major problems for collaborative filtering. To tackle these problems, some scholars have proposed to use deep neural network to extract text information, but did not consider the impact of long-distance dependent information and key information on their models. In this paper, we propose a neural collaborative filtering recommender method that integrates user and item auxiliary information. This method fully integrates user-item rating information, user assistance information and item text assistance information for feature extraction. First, Stacked Denoising Auto Encoder is used to extract user features, and Gated Recurrent Unit with auxiliary information is used to extract items’ latent vectors, respectively. The attention mechanism is used to learn key information when extracting text features. Second, the latent vectors learned by deep learning techniques are used in multi-layer nonlinear networks to learn more abstract and deeper feature representations to predict user preferences. According to the verification results on the MovieLens data set, the proposed model outperforms other traditional approaches and deep learning models making it state of the art.
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Bansal, Saurabh, and James S. Dyer. "Planning for End-User Substitution in Agribusiness." Operations Research 68, no. 4 (July 2020): 1000–1019. http://dx.doi.org/10.1287/opre.2019.1943.
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Saurabh Bansal and James S. Dyer study a common problem in the commercial agribusiness market, where farmers have a preference for a farm input such as a seed based on a fit with their geographical location but are also willing to accept a closely related substitute. Such consumer-driven choices may not be adequately represented by traditional models that maximize the profit of a firm that seeks to make substitutions while maximizing its profit. They use a set of recent results for evaluations of moments over polyhedra to determine the exact inventory levels a firm should keep of substitutable products. Using proprietary data from a large firm in this domain, they highlight the role of geographical and climate-related factors that affect product substitution in the agribusiness industry and identify specific regions in the United States where product substitution is a source of substantial revenue for firms.
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Adeel, Ahmad, Bruno Notteboom, Ansar Yasar, Kris Scheerlinck, and Jeroen Stevens. "Sustainable Streetscape and Built Environment Designs around BRT Stations: A Stated Choice Experiment Using 3D Visualizations." Sustainability 13, no. 12 (June 9, 2021): 6594. http://dx.doi.org/10.3390/su13126594.
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The incompatibility between the microscale-built environment designs around mass transit stations and stakeholders’ preferences causes dissatisfaction and inconvenience. The lack of a pedestrian-friendly environment, uncontrolled development patterns, traffic and parking issues make the street life vulnerable and unattractive for users, and affect the mass transit usage. How to design the streetscapes around mass transit stations to provide a user-friendly street environment is a crucial question to achieve sustainable transit-oriented development goals. To recognize the specific attributes of streetscape environment relevant in local context of BRT Lahore, this paper presents the results of a visual preference experiment in which nine attributes of built environment were systematically varied across choice sets. Multinomial logit models were set up to identify the preferences of three target groups: BRT users, commercial building users and residents at different locations. The research indicates that not only the road-related factors (bike lane and sidewalk widths, crossings facilities, street greenery) have a significant influence on people’s preference but also that building heights, and the typology of buildings and housing projects around BRT corridor have shaped these preferences. When planning and designing urban design projects around mass transit projects, these significant attributes should be considered.
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Fürnkranz, Johannes, Tomáš Kliegr, and Heiko Paulheim. "On cognitive preferences and the plausibility of rule-based models." Machine Learning 109, no. 4 (December 24, 2019): 853–98. http://dx.doi.org/10.1007/s10994-019-05856-5.
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AbstractIt is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models, simpler models are more interpretable than more complex ones. In this position paper, we question this latter assumption by focusing on one particular aspect of interpretability, namely the plausibility of models. Roughly speaking, we equate the plausibility of a model with the likeliness that a user accepts it as an explanation for a prediction. In particular, we argue that—all other things being equal—longer explanations may be more convincing than shorter ones, and that the predominant bias for shorter models, which is typically necessary for learning powerful discriminative models, may not be suitable when it comes to user acceptance of the learned models. To that end, we first recapitulate evidence for and against this postulate, and then report the results of an evaluation in a crowdsourcing study based on about 3000 judgments. The results do not reveal a strong preference for simple rules, whereas we can observe a weak preference for longer rules in some domains. We then relate these results to well-known cognitive biases such as the conjunction fallacy, the representative heuristic, or the recognition heuristic, and investigate their relation to rule length and plausibility.
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Xiang, Dan, and Zhijie Zhang. "Cross-Border E-Commerce Personalized Recommendation Based on Fuzzy Association Specifications Combined with Complex Preference Model." Mathematical Problems in Engineering 2020 (October 20, 2020): 1–9. http://dx.doi.org/10.1155/2020/8871126.
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Since cross-border e-commerce involves the export and import of commodities, it is affected by many policies and regulations, resulting in some special requirements for the recommendation system, which makes the traditional collaborative filtering recommendation algorithm less effective for the cross-border e-commerce recommendation system. To address this issue, a simple yet effective cross-border e-commerce personalized recommendation is proposed in this paper, which integrates fuzzy association rule and complex preference into a recommendation model. Under the constraint of fuzzy association rules, a hybrid recommendation model based on user complex preference features is constructed to mine user preference features, and personalized commodities recommendation is realized according to user behavior preference. Compared with the traditional recommendation algorithm, the improved algorithm reduces the impact of data sparsity. The experiment also verifies that the improved fuzzy association rule algorithm has a better recommendation effect than the existing state-of-the-art recommendation models. The recommendation system proposed in this paper has better generalization and has the performance to be applied to real-life scenarios.
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Onuean, Kittisak, Sunantha Sodsee, and Phayung Meesad. "Top-k Recommended Items: Applying Clustering Technique for Recommendation." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 12, no. 2 (February 19, 2019): 106–17. http://dx.doi.org/10.37936/ecti-cit.2018122.130537.
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This research proposes the Top-k Items Recommendation System which uses clustering techniques based on memory-based collaborative filtering technique. Currently, data sparsity and quantity of system are problems in memory-based collaborative filtering technique. We offer recommend or show some items set for user’s preference. In this research, we propose methods for recommended items set to user preference on data sparsity, movie lens datasets (1M) consisting of 671 users and 163,949 product items were used by determining the preference level between 1 and 5 and user satisfaction levels of all 98,903 items being build and test the models. Methods was divided into three parts included 1) Simple Agent Module 2) Neighbor Filtering and 3) Prediction and Recommend. Simple clustering was used to create a system to provide suggestions for sparsity data. Datasets obtained from clustering represented the sample agent of dataset to being create the recommendation system. Datasets were divided into two categories, 1) Traditional Data (TD) and 2) Statistic Data (SD), and each dataset clustered by k-means clustering. The experimental results demonstrated that the number of item types in the system were recommended in the TD and Euclidean (DIS). DIS was used to find the nearest value in TD for the item list recommendation to active users in the system with the a lot of number choice of recommendation system.
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Hawkes, G., Y. Malik, EM Dempsey, and CA Ryan. "PO-0626 Which Infant Mannequin Do You Prefer?: User Preference And Bag Mask Ability Of Different Mannequin Models." Archives of Disease in Childhood 99, Suppl 2 (October 2014): A459.1—A459. http://dx.doi.org/10.1136/archdischild-2014-307384.1268.
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Ge, Jun, Lei-lei Shi, Lu Liu, Hongwei Shi, and John Panneerselvam. "Intelligent Link Prediction Management Based on Community Discovery and User Behavior Preference in Online Social Networks." Wireless Communications and Mobile Computing 2021 (May 31, 2021): 1–13. http://dx.doi.org/10.1155/2021/3860083.
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Link prediction in online social networks intends to predict users who are yet to establish their network of friends, with the motivation of offering friend recommendation based on the current network structure and the attributes of nodes. However, many existing link prediction methods do not consider important information such as community characteristics, text information, and growth mechanism. In this paper, we propose an intelligent data management mechanism based on relationship strength according to the characteristics of social networks for achieving a reliable prediction in online social networks. Secondly, by considering the network structure attributes and interest preference of users as important factors affecting the link prediction process in online social networks, we propose further improvements in the prediction process by designing a friend recommendation model with a novel incorporation of the relationship information and interest preference characteristics of users into the community detection algorithm. Finally, extensive experiments conducted on a Twitter dataset demonstrate the effectiveness of our proposed models in both dynamic community detection and link prediction.
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Novi Wulansari, Dwi, and Milla Dwi Astari. "Mode choice analysis using discrete choice model from transport user (Case study: Jakarta LRT, Indonesia)." MATEC Web of Conferences 181 (2018): 03001. http://dx.doi.org/10.1051/matecconf/201818103001.
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Jakarta Light Rail Transit (Jakarta LRT) has been planned to be built as one of mass rail-based public transportation system in DKI Jakarta. The objective of this paper is to obtain a mode choice models that can explain the probability of choosing Jakarta LRT, and to estimate the sensitivity of mode choice if the attribute changes. Analysis of the research conducted by using discrete choice models approach to the behavior of individuals. Choice modes were observed between 1) Jakarta LRT and TransJakarta Bus, 2) Jakarta LRT and KRL-Commuter Jabodetabek. Mode choice model used is the Binomial Logit Model. The research data obtained through Stated Preference (SP) techniques. The model using the attribute influences such as tariff, travel time, headway and walking time. The models obtained are reliable and validated. Based on the results of the analysis shows that the most sensitive attributes affect the mode choice model is the tariff.
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Qiu, Lirong, and Jia Yu. "CLDA: An Effective Topic Model for Mining User Interest Preference under Big Data Background." Complexity 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/2503816.
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In the present big data background, how to effectively excavate useful information is the problem that big data is facing now. The purpose of this study is to construct a more effective method of mining interest preferences of users in a particular field in the context of today’s big data. We mainly use a large number of user text data from microblog to study. LDA is an effective method of text mining, but it will not play a very good role in applying LDA directly to a large number of short texts in microblog. In today’s more effective topic modeling project, short texts need to be aggregated into long texts to avoid data sparsity. However, aggregated short texts are mixed with a lot of noise, reducing the accuracy of mining the user’s interest preferences. In this paper, we propose Combining Latent Dirichlet Allocation (CLDA), a new topic model that can learn the potential topics of microblog short texts and long texts simultaneously. The data sparsity of short texts is avoided by aggregating long texts to assist in learning short texts. Short text filtering long text is reused to improve mining accuracy, making long texts and short texts effectively combined. Experimental results in a real microblog data set show that CLDA outperforms many advanced models in mining user interest, and we also confirm that CLDA also has good performance in recommending systems.
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Zhang, Depeng, Hongchen Wu, and Feng Yang. "FSCR: A Deep Social Recommendation Model for Misleading Information." Information 12, no. 1 (January 17, 2021): 37. http://dx.doi.org/10.3390/info12010037.
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The popularity of intelligent terminals and a variety of applications have led to the explosive growth of information on the Internet. Some of the information is real, some is not real, and may mislead people’s behaviors. Misleading information refers to false information made up by some malicious marketer to create panic and seek benefits. In particular, when emergency events break out, many users may be misled by the misleading information on the Internet, which further leads them to buy things that are not in line with their actual needs. We call this kind of human activity ‘emergency consumption’, which not only fails to reflect users’ true interests but also causes the phenomenon of user preference deviation, and thus lowers the accuracy of the personal recommender system. Although traditional recommendation models have proven useful in capturing users’ general interests from user–item interaction records, learning to predict user interest accurately is still a challenging problem due to the uncertainty inherent in user behavior and the limited information provided by user–item interaction records. In addition, to deal with the misleading information, we divide user information into two types, namely explicit preference information (explicit comments or ratings) and user side information (which can show users’ real interests and will not be easily affected by misleading information), and then we create a deep social recommendation model which fuses user side information called FSCR. The FSCR model is significantly better than existing baseline models in terms of rating prediction and system robustness, especially in the face of misleading information; it can effectively identify the misleading users and complete the task of rating prediction well.
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Huseyinov, Ilham, and Feride Savaroglu Tabak. "The Evaluation of Computer Algebra Systems Using Fuzzy Multi-Criteria Decision-Making Models." International Journal of Software Innovation 8, no. 1 (January 2020): 1–16. http://dx.doi.org/10.4018/ijsi.2020010101.
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The main purpose of this study is to present a systematic methodology based on fuzzy Multi-Criteria Decision-Making (FMCDM) models to help users evaluate computer algebra systems (CAS). CAS is a software package for the manipulation of mathematical formulas. The suggested methodology is user-centred which involves users' subjective evaluation judgments. User judgments are represented by means of fuzzy linguistic modelling techniques. An evaluation criteria framework based on the concept of the usefulness of CAS is developed. Next, two FMCDM models – fuzzy Analytical Hierarchy Process (FAHP) and fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) are proposed for the evaluation procedure. The FAHP is applied to determine the relative importance weights of qualitative evaluation criteria; the FTOPSIS is applied to rank the CAS alternatives. The illustrated case study demonstrates the applicability and effectiveness of the proposed methodology.
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Deng, Guang Biao. "Research on E-Commerce Promotion System Based on Web Data Mining Technology." Applied Mechanics and Materials 686 (October 2014): 311–15. http://dx.doi.org/10.4028/www.scientific.net/amm.686.311.
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This paper describes the use of Web data mining, and analyze the data on the web site (including the server log, commercial database, user database, the shopping cart, user mode) that access to relevant knowledge for goods, commodities such as preference relations. Secondly, the static model of the data mining methods, it is a manifestation of the site management personnel marketing thought. Based on these models, the paper proposed strategy for the site registered users, and produces the corresponding calculating formulas of a good recommendation and the corresponding recommendation algorithm for the current user, thus to get a user recommendation.
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Yu, Tong, Yilin Shen, and Hongxia Jin. "Towards Hands-Free Visual Dialog Interactive Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 1137–44. http://dx.doi.org/10.1609/aaai.v34i01.5465.
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With the recent advances of multimodal interactive recommendations, the users are able to express their preference by natural language feedback to the item images, to find the desired items. However, the existing systems either retrieve only one item or require the user to specify (e.g., by click or touch) the commented items from a list of recommendations in each user interaction. As a result, the users are not hands-free and the recommendations may be impractical. We propose a hands-free visual dialog recommender system to interactively recommend a list of items. At each time, the system shows a list of items with visual appearance. The user can comment on the list in natural language, to describe the desired features they further want. With these multimodal data, the system chooses another list of items to recommend. To understand the user preference from these multimodal data, we develop neural network models which identify the described items among the list and further predict the desired attributes. To achieve efficient interactive recommendations, we leverage the inferred user preference and further develop a novel bandit algorithm. Specifically, to avoid the system exploring more than needed, the desired attributes are utilized to reduce the exploration space. More importantly, to achieve sample efficient learning in this hands-free setting, we derive additional samples from the user's relative preference expressed in natural language and design a pairwise logistic loss in bandit learning. Our bandit model is jointly updated by the pairwise logistic loss on the additional samples derived from natural language feedback and the traditional logistic loss. The empirical results show that the probability of finding the desired items by our system is about 3 times as high as that by the traditional interactive recommenders, after a few user interactions.
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Garcia, Diogo F. V., Cristiane A. Domingues, Francisco S. Collet E. Silva, Newton D. Mori, Karen J. Brasel, John Kortbeek, Jameel Ali, and Renato S. Poggetti. "Efficacy of a Novel Surgical Manikin for Simulating Emergency Surgical Procedures." American Surgeon 85, no. 12 (December 2019): 1318–24. http://dx.doi.org/10.1177/000313481908501223.
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The practical component of the Advanced Trauma Life Support (ATLS®) course typically includes a TraumaMan® manikin. This manikin is expensive; hence, a low-cost alternative (SurgeMan®) was developed in Brazil. Our primary objective was to compare user satisfaction among Surge-Man, TraumaMan, and porcine models during the course. Our secondary objective was to determine the user satisfaction scores for SurgeMan. This study included 36 ATLS students and nine instructors (4:1 ratio). Tube thoracostomy, cricothyroidotomy, pericardiocentesis, and diagnostic peritoneal lavage were performed on all the three models. The participants then rated their satisfaction both after each activity and after the course. The porcine and TraumaMan models fared better than SurgeMan for all skills except pericardiocentesis. In the absence of ethical or financial constraints, 58 per cent of the students and 66 per cent of the instructors indicated preference for the porcine model. When ethical and financial factors were considered, no preference was evident among the students, whereas 66 per cent of instructors preferred SurgeMan over the others. The students gave all three models an overall adequacy rating of >80 per cent; the instructors gave only the animal models an adequacy rating of <80 per cent. Although the users were more satisfied with TraumaMan than with SurgeMan, both were considered acceptable for the ATLS course.
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Shams, Kollol, Xia Jin, Rickey Fitzgerald, Hamidreza Asgari, and M. S. Hossan. "Value of Reliability for Road Freight Transportation." Transportation Research Record: Journal of the Transportation Research Board 2610, no. 1 (January 2017): 35–43. http://dx.doi.org/10.3141/2610-05.
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This paper presents the findings of a study recently conducted in Florida to quantify freight users’ willingness to pay (WTP) for the improvement of transportation-related attributes, particularly reliability. A stated preference survey was developed and administered between January and May 2016. The survey collected responses from 150 shippers, carriers, and forwarders. Econometric models, including mixed and multinomial logit models, were developed to estimate the users’ WTP and to investigate the presence of user heterogeneity. The value of time and the value of reliability were estimated separately for the various user groups. The results indicated that carriers showed the lowest WTP when their WTP was compared with that of other freight users. Shippers without transportation—that is, shippers who contracted out their shipping— exhibited more interest in reducing travel time savings, whereas shippers with transportation showed more sensitivity to reliability. Preference heterogeneity was also explored by commodity group and product type. The results confirmed the findings from past studies and showed significant differences in WTP values when the sources of heterogeneity were considered. This paper contributes to the literature by providing empirical evidence of the quantification of the value of reliability in road freight transportation and the impacts of user heterogeneity. The study results will help advance understanding of the impacts of the performance of transportation systems on the freight industry.
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Hwang, Tae-Gyu, and Sung Kwon Kim. "Movie Recommendation through Multiple Bias Analysis." Applied Sciences 11, no. 6 (March 22, 2021): 2817. http://dx.doi.org/10.3390/app11062817.
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A recommender system (RS) refers to an agent that recommends items that are suitable for users, and it is implemented through collaborative filtering (CF). CF has a limitation in improving the accuracy of recommendations based on matrix factorization (MF). Therefore, a new method is required for analyzing preference patterns, which could not be derived by existing studies. This study aimed at solving the existing problems through bias analysis. By analyzing users’ and items’ biases of user preferences, the bias-based predictor (BBP) was developed and shown to outperform memory-based CF. In this paper, in order to enhance BBP, multiple bias analysis (MBA) was proposed to efficiently reflect the decision-making in real world. The experimental results using movie data revealed that MBA enhanced BBP accuracy, and that the hybrid models outperformed MF and SVD++. Based on this result, MBA is expected to improve performance when used as a system in related studies and provide useful knowledge in any areas that need features that can represent users.
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Lin, Chen, Xiaolin Shen, Si Chen, Muhua Zhu, and Yanghua Xiao. "Non-Compensatory Psychological Models for Recommender Systems." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4304–11. http://dx.doi.org/10.1609/aaai.v33i01.33014304.
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The study of consumer psychology reveals two categories of consumption decision procedures: compensatory rules and non-compensatory rules. Existing recommendation models which are based on latent factor models assume the consumers follow the compensatory rules, i.e. they evaluate an item over multiple aspects and compute a weighted or/and summated score which is used to derive the rating or ranking of the item. However, it has been shown in the literature of consumer behavior that, consumers adopt non-compensatory rules more often than compensatory rules. Our main contribution in this paper is to study the unexplored area of utilizing non-compensatory rules in recommendation models.Our general assumptions are (1) there are K universal hidden aspects. In each evaluation session, only one aspect is chosen as the prominent aspect according to user preference. (2) Evaluations over prominent and non-prominent aspects are non-compensatory. Evaluation is mainly based on item performance on the prominent aspect. For non-prominent aspects the user sets a minimal acceptable threshold. We give a conceptual model for these general assumptions. We show how this conceptual model can be realized in both pointwise rating prediction models and pair-wise ranking prediction models. Experiments on real-world data sets validate that adopting non-compensatory rules improves recommendation performance for both rating and ranking models.
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Lyu, Zequn, Yu Dong, Chengfu Huo, and Weijun Ren. "Deep Match to Rank Model for Personalized Click-Through Rate Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 156–63. http://dx.doi.org/10.1609/aaai.v34i01.5346.
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Click-through rate (CTR) prediction is a core task in the field of recommender system and many other applications. For CTR prediction model, personalization is the key to improve the performance and enhance the user experience. Recently, several models are proposed to extract user interest from user behavior data which reflects user's personalized preference implicitly. However, existing works in the field of CTR prediction mainly focus on user representation and pay less attention on representing the relevance between user and item, which directly measures the intensity of user's preference on target item. Motivated by this, we propose a novel model named Deep Match to Rank (DMR) which combines the thought of collaborative filtering in matching methods for the ranking task in CTR prediction. In DMR, we design User-to-Item Network and Item-to-Item Network to represent the relevance in two forms. In User-to-Item Network, we represent the relevance between user and item by inner product of the corresponding representation in the embedding space. Meanwhile, an auxiliary match network is presented to supervise the training and push larger inner product to represent higher relevance. In Item-to-Item Network, we first calculate the item-to-item similarities between user interacted items and target item by attention mechanism, and then sum up the similarities to obtain another form of user-to-item relevance. We conduct extensive experiments on both public and industrial datasets to validate the effectiveness of our model, which outperforms the state-of-art models significantly.
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Salonen, Ville, and Heikki Karjaluoto. "About time." Journal of Systems and Information Technology 21, no. 2 (May 13, 2019): 236–54. http://dx.doi.org/10.1108/jsit-06-2017-0042.
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Purpose The purpose of this paper seeks to develop a motivation-based complementary framework for temporally dynamic user preferences to facilitate optimal timing in web personalisation. It also aims to highlight the benefits of considering user motivation when addressing issues in temporal dynamics. Design/methodology/approach Through theory, a complementary framework and propositions for motivation-based temporal dynamics for further testing are created. The framework is validated by feeding back findings, whereas some of the propositions are validated through an experiment. Findings The suggested framework distinguishes two ways (identifying/learning and shifting) of using a motive-based approach to temporal dynamics in web personalisation. The suggested outcomes include enhanced timing in matching current preferences and improved conversion. Validation measures predominantly support both the framework and the tested propositions. The theoretical basis for the approach paves a path towards refined psychological user models; however, currently on a complementary level. Research limitations/implications While the framework is validated through feeding back findings, and some of the propositions are validated through basic experimentation, further empirical testing is required. Practical implications A generalised approach for complementing personalisation procedures with motivation-based temporal dynamics is offered, with implications for both user modelling and preference matching. Originality/value This paper offers novel insights to web personalisation by considering the in-depth effects of user motivation.
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Luo, Pengcheng, Jilin Zhang, Jian Wan, Nailiang Zhao, Zujie Ren, Li Zhou, and Jing Shen. "A mobile services recommendation system fuses implicit and explicit user trust relationships." Journal of Ambient Intelligence and Smart Environments 13, no. 1 (January 20, 2021): 21–35. http://dx.doi.org/10.3233/ais-200585.
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In recent years, with the development of advanced mobile applications, people’s various daily behavior data, such as geographic location, social information, hobbies, are more easily collected. To process these data, data cross-boundary fusion has become a key technology, and there are some challenges, such as solving the problems of the cross-boundary business integrity, cross-boundary value complementarity and so on. Mobile Services Recommendation requires improved recommendation accuracy. User trust is an effective measure of information similarity between users. Using trust can effectively improve the accuracy of recommendations. The existing methods have low utilization of general trust data, sparseness of trust data, and lack of user trust characteristics. Therefore, a method needs to be proposed to make up for the shortcomings of explicit trust relationships and improve the accuracy of user interest feature completion. In this paper, a recommendation model is proposed to mine the implicit trust relationships from user data and integrate the explicit social information of users. First, the rating prediction model was improved using the traditional Singular Value Decomposition (SVD) model, and the implicit trust relationships were mined from the user’s historical data. Then, they were fused with the explicit social trust relationships to obtain a crossover data fusion model. We tested the model using three different orders of magnitude. We compared the user preference prediction accuracies of two models: one that does not integrate social information and one that integrates social information. The results show that our model improves the user preference prediction accuracy and has higher accuracy for cold start users. On the three data sets, the average error is reduced by 2.29%, 5.44% and 4.42%, suggesting that it is an effective data crossover fusion technology.
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Zhao, Lei, Hongzhi Guan, Xinjie Zhang, and Xiongbin Wu. "A regret-based route choice model with asymmetric preference in a stochastic network." Advances in Mechanical Engineering 10, no. 8 (August 2018): 168781401879323. http://dx.doi.org/10.1177/1687814018793238.
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In this study, a stochastic user equilibrium model on the modified random regret minimization is proposed by incorporating the asymmetric preference for gains and losses to describe its effects on the regret degree of travelers. Travelers are considered to be capable of perceiving the gains and losses of attributes separately when comparing between the alternatives. Compared to the stochastic user equilibrium model on the random regret minimization model, the potential difference of emotion experienced induced by the loss and gain in the equal size is jointly caused by the taste parameter and loss aversion of travelers in the proposed model. And travelers always tend to use the routes with the minimum perceived regret in the travel decision processes. In addition, the variational inequality problem of the stochastic user equilibrium model on the modified random regret minimization model is given, and the characteristics of its solution are discussed. A route-based solution algorithm is used to resolve the problem. Numerical results given by a three-route network show that the loss aversion produces a great impact on travelers’ choice decisions and the model can more flexibly capture the choice behavior than the existing models.