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Journal articles on the topic 'User Preference Models'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Luo, Mingshi, Xiaoli Zhang, Jiao Li, Peipei Duan, and Shengnan Lu. "User Dynamic Preference Construction Method Based on Behavior Sequence." Scientific Programming 2022 (July 22, 2022): 1–15. http://dx.doi.org/10.1155/2022/6101045.

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People’s needs are constantly changing, and the performance of traditional recommendation algorithms is no longer enough to meet the demand. Considering that users’ preferences change with time, the users’ behavior sequence hides the evolution and change law of users’ preferences, so mining the dependence of the users’ behavior sequence is extremely important to predict users’ dynamic preferences. From the perspective of constructing users’ dynamic preferences, this paper proposes a users’ dynamic preference model based on users’ behavior sequences. Firstly, the user’s interest model is divide

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Savia, Eerika, Kai Puolamäki, and Samuel Kaski. "Latent grouping models for user preference prediction." Machine Learning 74, no. 1 (2008): 75–109. http://dx.doi.org/10.1007/s10994-008-5081-7.

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Yang, Taoru, Yong Gao, Zhou Huang, and Yu Liu. "UPTDNet: A User Preference Transfer and Drift Network for Cross-City Next POI Recommendation." International Journal of Intelligent Systems 2023 (March 4, 2023): 1–17. http://dx.doi.org/10.1155/2023/9091570.

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Cross-city point of interest (POI) recommendation for tourists in an unfamiliar city has high application value but is challenging due to the data sparsity. Most existing models attempt to alleviate the sparsity problem by learning the user preference transfer and drift. However, they either fail to simultaneously model the preference transfer and drift in both long- and short-term user preferences or cannot accomplish the task of the next POI recommendation, which is crucial for a wide spectrum of applications ranging from transportation and urban planning to advertising. To address the limit

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Wu, Weidong, Xiaoyan Sun, Guangyi Man, Shuai Li, and Lin Bao. "Interactive Multifactorial Evolutionary Optimization Algorithm with Multidimensional Preference Surrogate Models for Personalized Recommendation." Applied Sciences 13, no. 4 (2023): 2243. http://dx.doi.org/10.3390/app13042243.

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Interactive evolutionary algorithms (IEAs) coupled with a data-driven user surrogate model (USM) have recently been proposed for enhancing personalized recommendation performance. Since the USM relies on only one model to describe the full range of user preferences, existing USMbased IEAs have not investigated how knowledge migrates between preference models to improve the diversity and novelty of recommendations. Motivated by this, an interactive multifactorial evolutionary optimization algorithm with multidimensional preference user surrogate models is proposed here to perform a multi-view o

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Zhang, Wumei, Jianping Zhang, and Yongzhen Zhang. "A Comment Aspect-Level User Preference Transfer Model for Cross-Domain Recommendations." Information Resources Management Journal 37, no. 1 (2024): 1–25. http://dx.doi.org/10.4018/irmj.345360.

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Traditional cross-domain recommendation models make it difficult to deeply mine users' aspect-level preferences from comment information due to existing problems such as polysemy of comment text, sparse comment data, and user cold start. A Cross-Domain Recommender (CDR) model that integrates comment knowledge enhancement and aspect-level user preference transfer (C-KE-AUT) was proposed to address the above issues. Firstly, an aspect-level user preference extraction model was constructed by combining the RoBERTa word embedding model, high-level feature representation based on Transformer, and a

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Aliev, A., and Z. Maharramov. "FEATURES OF BUILDING MODELS OF USER PREFERENCES FOR CLOUD SERVICES AND THEIR CLASSIFICATION." Sciences of Europe, no. 113 (March 27, 2023): 82–85. https://doi.org/10.5281/zenodo.7773850.

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User preference models are critical in understanding how users interact with cloud services. By studying factors such as cost, performance, security, ease of use, and reliability, these models offer insights into what motivates users to adopt cloud services and what factors may prevent them from doing so. Cloud service providers can use these models to deliver personalized and relevant content, optimize service delivery, and ensure user privacy and security. In the article, on the example of an airline, experimental data are collected and their classification is considered using the confusion

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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 (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 re

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Ye, Yuyang, Zhi Zheng, Yishan Shen, et al. "Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 12 (2025): 13069–77. https://doi.org/10.1609/aaai.v39i12.33426.

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Recent advances in Large Language Models (LLMs) have demonstrated significant potential in the field of Recommendation Systems (RSs). Most existing studies have focused on converting user behavior logs into textual prompts and leveraging techniques such as prompt tuning to enable LLMs for recommendation tasks. Meanwhile, research interest has recently grown in multimodal recommendation systems that integrate data from images, text, and other sources using modality fusion techniques. This introduces new challenges to the existing LLM-based recommendation paradigm which relies solely on text mod

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Rivera-Abarca, Ana Lucía, Jazmín Isabel García-Guerra, Héctor Oswaldo Aguilar-Cajas, Heidy Elizabeth Vergara-Zurita, José Israel López-Pumalema, and Freddy Armijos-Arcos. "Predictive Models of Typographic Preference in Digital Media." Data and Metadata 4 (June 3, 2025): 1062. https://doi.org/10.56294/dm20251062.

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Introduction: This article explores how typography influences user experience in digital environments, highlighting its evolution from the 11th century to the Internet era. Objective: The aim of this research was to examine the psychological impact of fonts, which evoke emotional responses and affect readability, design and user behavior. Methodology: Predictive models, such as regression, classification and time series, are used to analyze typographic preferences, helping designers to optimize digital interfaces. Results: The study simulated data from 1,000 participants, considering variables

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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 (1991): 281–91. http://dx.doi.org/10.1007/bf02404078.

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Izawa, Shin, Keiko Ono, and Panagiotis Adamidis. "Diversifying Furniture Recommendations: A User-Profile-Enhanced Recommender VAE Approach." Applied Sciences 15, no. 5 (2025): 2761. https://doi.org/10.3390/app15052761.

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We propose a novel recommendation model for diversifying furniture recommendations and aligning them more closely with user preferences. Our model builds upon the Recommender Variational Autoencoder (RecVAE), known for its effectiveness and ability to overcome overfitting by linking user feedback with user representation. However, since RecVAE relies on implicit feedback data, it tends to exhibit bias towards popular items, potentially creating a recommendation filter bubble. While previous work has proposed user profiles learned from a user’s personal information and the textual data of an it

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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 n

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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 (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 ap

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Shang, Fu, Fanyi Zhao, Mingxuan Zhang, un Sun, and Jiatu Shi. "Personalized Recommendation Systems Powered By Large Language Models: Integrating Semantic Understanding and User Preferences." International Journal of Innovative Research in Engineering and Management 11, no. 4 (2024): 39–49. http://dx.doi.org/10.55524/ijirem.2024.11.4.6.

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This study proposes a novel personalized recommendation system leveraging Large Language Models (LLMs) to integrate semantic understanding with user preferences [1]. The system addresses critical challenges in traditional recommendation approaches by harnessing LLMs' advanced natural language processing capabilities. We introduce a framework combining a fine-tuned Roberta semantic analysis model with a multi-modal user preference extraction mechanism.The LLM component undergoes domain adaptation using Masked Language Modeling on a corpus of 112,000 user reviews from the MyAnimeList dataset, fo

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Alsaleh, Nael, Bilal Farooq, Yixue Zhang, and Steven Farber. "On-demand transit user preference analysis using hybrid choice models." Journal of Choice Modelling 49 (December 2023): 100451. http://dx.doi.org/10.1016/j.jocm.2023.100451.

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Cai, Wanxin, Mingqing Yang, and Li Lin. "An Inspiration Recommendation System for Automotive Styling Design Based on User Behavior Data and Group Preferences." Systems 12, no. 11 (2024): 491. http://dx.doi.org/10.3390/systems12110491.

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Group preferences are crucial for Inspirational Solutions of Automotive Design (ISAD). However, sparse individual purchase behavior hinders the identification of group preferences. Therefore, a novel inspiration recommendation (IR) system based on multi-level mining of user behavior data is proposed. Firstly, the K-means algorithm is employed to cluster users based on a variety of features. The fixed association rule is then applied to filter and identify relevant subsets, forming the foundational basis for constructing a user portrait. The Nonlinear Bayesian Personalized Ranking (NBPR) is con

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Zhang, Bingjie, Junchao Yu, Zhe Kang, Tianyu Wei, Xiaoyu Liu, and Suhua Wang. "An adaptive preference retention collaborative filtering algorithm based on graph convolutional method." Electronic Research Archive 31, no. 2 (2022): 793–811. http://dx.doi.org/10.3934/era.2023040.

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<abstract> <p>Collaborative filtering is one of the most widely used methods in recommender systems. In recent years, Graph Neural Networks (GNN) were naturally applied to collaborative filtering methods to model users' preference representation. However, empirical research has ignored the effects of different items on user representation, which prevented them from capturing fine-grained users' preferences. Besides, due to the problem of data sparsity in collaborative filtering, most GNN-based models conduct a large number of graph convolution operations in the user-item graph, res

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Chang, Ying-Ying, Wei-Yao Wang, and Wen-Chih Peng. "SeGA: Preference-Aware Self-Contrastive Learning with Prompts for Anomalous User Detection on Twitter." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 1 (2024): 30–37. http://dx.doi.org/10.1609/aaai.v38i1.27752.

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In the dynamic and rapidly evolving world of social media, detecting anomalous users has become a crucial task to address malicious activities such as misinformation and cyberbullying. As the increasing number of anomalous users improves the ability to mimic normal users and evade detection, existing methods only focusing on bot detection are ineffective in terms of capturing subtle distinctions between users. To address these challenges, we proposed SeGA, preference-aware self-contrastive learning for anomalous user detection, which leverages heterogeneous entities and their relations in the

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Liu, Huazhen, Wei Wang, Yihan Zhang, Renqian Gu, and Yaqi Hao. "Neural Matrix Factorization Recommendation for User Preference Prediction Based on Explicit and Implicit Feedback." Computational Intelligence and Neuroscience 2022 (January 10, 2022): 1–12. http://dx.doi.org/10.1155/2022/9593957.

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Explicit feedback and implicit feedback are two important types of heterogeneous data for constructing a recommendation system. The combination of the two can effectively improve the performance of the recommendation system. However, most of the current deep learning recommendation models fail to fully exploit the complementary advantages of two types of data combined and usually only use binary implicit feedback data. Thus, this paper proposes a neural matrix factorization recommendation algorithm (EINMF) based on explicit-implicit feedback. First, neural network is used to learn nonlinear fe

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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 (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 r

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Yamasaki, Haruto, Masaki Matsubara, Hiroyoshi Ito, et al. "A Cluster-Aware Transfer Learning for Bayesian Optimization of Personalized Preference Models." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 11, no. 1 (2023): 175–85. http://dx.doi.org/10.1609/hcomp.v11i1.27558.

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Obtaining personalized models of the crowd is an important issue in various applications, such as preference acquisition and user interaction customization. However, the crowd setting, in which we assume we have little knowledge about the person, brings the cold start problem, which may cause avoidable unpreferable interactions with the people. This paper proposes a cluster-aware transfer learning method for the Bayesian optimization of personalized models. The proposed method, called Cluster-aware Bayesian Optimization, is designed based on a known feature: user preferences are not completely

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Azri, Abdelghani, Adil Haddi, and Hakim Allali. "IUAutoTimeSVD: A Hybrid Temporal Recommender System Integrating Item and User Features Using a Contractive Autoencoder ++." Information 15, no. 4 (2024): 204. http://dx.doi.org/10.3390/info15040204.

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Collaborative filtering (CF), a fundamental technique in personalized Recommender Systems, operates by leveraging user–item preference interactions. Matrix factorization remains one of the most prevalent CF-based methods. However, recent advancements in deep learning have spurred the development of hybrid models, which extend matrix factorization, particularly with autoencoders, to capture nonlinear item relationships. Despite these advancements, many proposed models often neglect dynamic changes in the rating process and overlook user features. This paper introduces IUAutoTimeSVD++, a novel h

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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 (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 abrup

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Modak, Sadanand, Noah Tobias Patton, Isil Dillig, and Joydeep Biswas. "SYNAPSE: SYmbolic Neural-Aided Preference Synthesis Engine." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 26 (2025): 27529–37. https://doi.org/10.1609/aaai.v39i26.34965.

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This paper addresses the problem of preference learning, which aims to align robot behaviors through learning user-specific preferences (e.g. “good pull-over location”) from visual demonstrations. Despite its similarity to learning factual concepts (e.g. “red door”), preference learning is a fundamentally harder problem due to its subjective nature and the paucity of person-specific training data. We address this problem using a novel framework called SYNAPSE, which is a neuro-symbolic approach designed to efficiently learn preferential concepts from limited data. SYNAPSE represents preference

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SadighZadeh, Saeid, and Marjan Kaedi. "Modeling user preferences in online stores based on user mouse behavior on page elements." Journal of Systems and Information Technology 24, no. 2 (2022): 112–30. http://dx.doi.org/10.1108/jsit-12-2019-0264.

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Purpose Online businesses require a deep understanding of their customers’ interests to innovate and develop new products and services. Users, on the other hand, rarely express their interests explicitly. The purpose of this study is to predict users’ implicit interest in products of an online store based on their mouse behavior through various product page elements. Design/methodology/approach First, user mouse behavior data is collected throughout an online store website. Next, several mouse behavioral features on the product pages elements are extracted and finally, several models are extra

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Li, Ruijing, Jianzhong Guo, Chun Liu, Zheng Li, and Shaoqing Zhang. "Using Attributes Explicitly Reflecting User Preference in a Self-Attention Network for Next POI Recommendation." ISPRS International Journal of Geo-Information 11, no. 8 (2022): 440. http://dx.doi.org/10.3390/ijgi11080440.

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With the popularity of location-based social networks such as Weibo and Twitter, there are many records of points of interest (POIs) showing when and where people have visited certain locations. From these records, next POI recommendation suggests the next POI that a target user might want to visit based on their check-in history and current spatio-temporal context. Current next POI recommendation methods mainly apply different deep learning models to capture user preferences by learning the nonlinear relations between POIs and user preference and pay little attention to mining or using the in

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Guo, Shangzhi, Xiaofeng Liao, Fei Meng, et al. "FSASA: Sequential recommendation based on fusing session-aware models and self-attention networks." Computer Science and Information Systems, no. 00 (2023): 67. http://dx.doi.org/10.2298/csis230522067g.

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The recommendation system can alleviate the problem of ?information overload?, tap the potential value of data, push personalized information to users in need, and improve information utilization. Sequence recommendation has become a hot research direction because of its practicality and high precision. Deep Neural Networks (DNN) have the natural advantage of capturing comprehensive relations among different entities, thus almost occupying a dominant position in sequence recommendation in the past few years. However, as Deep Learning (DL)-based methods are widely used to model local preference

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T., Vijaya Chithra, and Yasmeen A. "USER PREFERENCE ON MOBILE APPLICATIONS AMONG COLLEGE STUDENTS." International Journal of Current Research and Modern Education 3, no. 1 (2018): 23–26. https://doi.org/10.5281/zenodo.1145479.

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Mobile has become an integral part of every individual of this generation. It has also been considered as a basic need of everyone. Usage of Mobile applications equally plays a vital role among the users of various mobile models available in the market. Mobile Applications in the earlier days were originally offered for general productivity and information retrieval, including E-mail, drive calendar, contacts, stock market and whether information. But the service of mobile applications now-a-days has been widening to a larger extent and hence the objective of the present study is based on the

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Ju, Hyunjun, SeongKu Kang, Dongha Lee, Junyoung Hwang, Sanghwan Jang, and Hwanjo Yu. "Multi-Domain Recommendation to Attract Users via Domain Preference Modeling." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (2024): 8582–90. http://dx.doi.org/10.1609/aaai.v38i8.28702.

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Recently, web platforms are operating various service domains simultaneously. Targeting a platform that operates multiple service domains, we introduce a new task, Multi-Domain Recommendation to Attract Users (MDRAU), which recommends items from multiple ``unseen'' domains with which each user has not interacted yet, by using knowledge from the user's ``seen'' domains. In this paper, we point out two challenges of MDRAU task. First, there are numerous possible combinations of mappings from seen to unseen domains because users have usually interacted with a different subset of service domains.

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Mukta, Md Saddam Hossain, Euna Mehnaz Khan, Mohammed Eunus Ali, and Jalal Mahmud. "Predicting Movie Genre Preferences from Personality and Values of Social Media Users." Proceedings of the International AAAI Conference on Web and Social Media 11, no. 1 (2017): 624–27. http://dx.doi.org/10.1609/icwsm.v11i1.14910.

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We propose a novel technique to predict a user’s movie genre preference from her psycholinguistic attributes obtained from user social media interactions. In particular, we build machine learning based classiﬁcation models that take user tweets as input to derive her psychological attributes: personality and value scores, and gives her movie genre preference as output. We train these models using user tweets in Twitter, and her reviews and ratings of movies of different genres in Internet movie database (IMDb). We exploit a key concept of psychology, i.e., an individual’s personality and value

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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 (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

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Yue, Weiqi, Yuyu Yin, Xin Zhang, Binbin Shi, Tingting Liang, and Jian Wan. "CoT4Rec: Revealing User Preferences Through Chain of Thought for Recommender Systems." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 12 (2025): 13142–51. https://doi.org/10.1609/aaai.v39i12.33434.

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Large Language Models (LLMs) offer groundbreaking advancements in recommender systems through superior text analysis and decision-making support. However, integrating LLMs into recommender systems still suffers from the problems of identifier uninterpretability and lack of transparency. To address these issues and fully leverage the capabilities of LLMs, we propose a chain of thought (CoT) based recommendation framework called CoT4Rec which employs LLMs as data enhancers for user preference analysis. Initially, we design a CoT reasoning strategy that can derive more behaviorally-aligned user p

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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 (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

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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 functiona

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Kang, Seongju, and Kwangsue Chung. "Preference-Tree-Based Real-Time Recommendation System." Entropy 24, no. 4 (2022): 503. http://dx.doi.org/10.3390/e24040503.

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In the current era of online information overload, recommendation systems are very useful for helping users locate content that may be of interest to them. A personalized recommendation system presents content based on information such as a user’s browsing history and the videos watched. However, information filtering-based recommendation systems are vulnerable to data sparsity and cold-start problems. Additionally, existing recommendation systems suffer from the large overhead incurred in learning regression models used for preference prediction or in selecting groups of similar users. In thi

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Gu, Jie, Feng Wang, Qinghui Sun, et al. "Exploiting Behavioral Consistence for Universal User Representation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (2021): 4063–71. http://dx.doi.org/10.1609/aaai.v35i5.16527.

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User modeling is critical for developing personalized services in industry. A common way for user modeling is to learn user representations that can be distinguished by their interests or preferences. In this work, we focus on developing universal user representation model. The obtained universal representations are expected to contain rich information, and be applicable to various downstream applications without further modifications (e.g., user preference prediction and user profiling). Accordingly, we can be free from the heavy work of training task-specific models for every downstream task

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Zhang, Chunxu, Guodong Long, Hongkuan Guo, et al. "Multifaceted User Modeling in Recommendation: A Federated Foundation Models Approach." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 12 (2025): 13197–205. https://doi.org/10.1609/aaai.v39i12.33440.

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Multifaceted user modeling aims to uncover fine-grained patterns and learn representations from user data, revealing their diverse interests and characteristics, such as profile, preference, and personality. Recent studies on foundation model-based recommendation have emphasized the Transformer architecture's remarkable ability to capture complex, non-linear user-item interaction relationships. This paper aims to advance foundation model-based recommendersystems by introducing enhancements to multifaceted user modeling capabilities. We propose a novel Transformer layer designed specifically fo

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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 (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 mode

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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 (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 mo

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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 mo

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Wei, Chunting, Jiwei Qin, and Qiulin Ren. "A Ranking Recommendation Algorithm Based on Dynamic User Preference." Sensors 22, no. 22 (2022): 8683. http://dx.doi.org/10.3390/s22228683.

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In recent years, hybrid recommendation techniques based on feature fusion have gained extensive attention in the field of list ranking. Most of them fuse linear and nonlinear models to simultaneously learn the linear and nonlinear features of entities and jointly fit user-item interactions. These methods are based on implicit feedback, which can reduce the difficulty of data collection and the time of data preprocessing, but will lead to the lack of entity interaction depth information due to the lack of user satisfaction. This is equivalent to artificially reducing the entity interaction feat

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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 (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 fro

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Porcelli, Lorenzo, Michele Mastroianni, Massimo Ficco, and Francesco Palmieri. "A User-Centered Privacy Policy Management System for Automatic Consent on Cookie Banners." Computers 13, no. 2 (2024): 43. http://dx.doi.org/10.3390/computers13020043.

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Despite growing concerns about privacy and an evolution in laws protecting users’ rights, there remains a gap between how industries manage data and how users can express their preferences. This imbalance often favors industries, forcing users to repeatedly define their privacy preferences each time they access a new website. This process contributes to the privacy paradox. We propose a user support tool named the User Privacy Preference Management System (UPPMS) that eliminates the need for users to handle intricate banners or deceptive patterns. We have set up a process to guide even a non-e

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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 (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 cy

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Kweon, Wonbin, SeongKu Kang, and Hwanjo Yu. "Obtaining Calibrated Probabilities with Personalized Ranking Models." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (2022): 4083–91. http://dx.doi.org/10.1609/aaai.v36i4.20326.

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For personalized ranking models, the well-calibrated probability of an item being preferred by a user has great practical value. While existing work shows promising results in image classification, probability calibration has not been much explored for personalized ranking. In this paper, we aim to estimate the calibrated probability of how likely a user will prefer an item. We investigate various parametric distributions and propose two parametric calibration methods, namely Gaussian calibration and Gamma calibration. Each proposed method can be seen as a post-processing function that maps th

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McCole, Dan, Tatiana A. Iretskaia, Elizabeth E. Perry, Jungho Suh, and John Noyes. "Park Design Informed by Stated Preference Choice: Integrating User Perspectives into the Development of an Off-Road Vehicle Park in Michigan." Land 11, no. 11 (2022): 1950. http://dx.doi.org/10.3390/land11111950.

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At a time when many public park and recreational programs are required by local governments to be financially self-sustaining, it is critical for planners to design a new development with the end-user in mind. Feasibility studies often either do not examine user preferences or use Likert-type surveys to investigate features in isolation without evaluating trade-offs from financial and finite space limitations. This study used stated preference choice method (SPCM) to inform the initial design of an off-road vehicle (ORV) park. The park was developed near Detroit, Michigan, a metropolitan area

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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 (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 lea

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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 (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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Mahaboobsubani, Shaik. "AI-Driven Personalization in Hospitality Booking Platforms." Journal of Scientific and Engineering Research 8, no. 10 (2021): 223–30. https://doi.org/10.5281/zenodo.14356522.

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The use of AI in the hospitality sector has moved booking platforms into a direction of offering extremely personalized experiences. The article discusses how AI-driven recommendation systems produce booking options tailored to user preferences as a way to further increase engagement and user satisfaction. Different machine learning models are investigated to assess the effectiveness in predicting user behavior and preference, including collaborative filtering, content-based filtering, and hybrid approaches. Comparative studies indicate that the level of engagement and booking conversion is si

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Man, Guangyi, Xiaoyan Sun, and Weidong Wu. "Vectorized Representation of Commodities by Fusing Multisource Heterogeneous User-Generated Content with Multiple Models." Applied Sciences 13, no. 7 (2023): 4217. http://dx.doi.org/10.3390/app13074217.

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In the field of personalized recommendation, user-generated content (UGC) such as videos, images, and product comments are becoming increasingly important, since they implicitly represent the preferences of users. The vectorized representation of a commodity with multisource and heterogeneous UGC is the key for sufficiently mining the preference information to make a recommendation. Existing studies have mostly focused on using one type of UGC, e.g., images, to enrich the representation of a commodity, ignoring other contents. When more UGC are fused, complicated models with heavy computation

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