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Journal articles on the topic 'Users’ preferences'

Author: Grafiati

Published: 10 December 2022

Last updated: 31 July 2025

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1

Shi, Yancui, Jianhua Cao, Congcong Xiong, and Xiankun Zhang. "A Prediction Method of Mobile User Preference Based on the Influence between Users." International Journal of Digital Multimedia Broadcasting 2018 (July 19, 2018): 1–12. http://dx.doi.org/10.1155/2018/8081409.

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User preference will be impacted by other users. To accurately predict mobile user preference, the influence between users is introduced into the prediction model of user preference. First, the mobile social network is constructed according to the interaction behavior of the mobile user, and the influence of the user is calculated according to the topology of the constructed mobile social network and mobile user behavior. Second, the influence between users is calculated according to the user’s influence, the interaction behavior between users, and the similarity of user preferences. When calc

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Park, Han-Saem, Moon-Hee Park, and Sung-Bae Cho. "Mobile Information Recommendation Using Multi-Criteria Decision Making with Bayesian Network." International Journal of Information Technology & Decision Making 14, no. 02 (2015): 317–38. http://dx.doi.org/10.1142/s0219622015500017.

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The advancement of network technology and the popularization of the Internet lead to increased interest in information recommendation. This paper proposes a group recommendation system that takes the preferences of group users in mobile environment and applies the system to recommendation of restaurants. The proposed system recommends the restaurants by considering various preferences of multiple users. To cope with the uncertainty in mobile environment, we exploit Bayesian network, which provides reliable performance and models individual user's preference. Also, Analytical Hierarchy Process

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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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Huiyuan Li, Zhengang Li, Tao Jin, et al. "Convolution Serialization Recommendation with Time Characteristics and User Preferences." International Journal of Advanced Networking and Applications 15, no. 06 (2024): 6156–62. http://dx.doi.org/10.35444/ijana.2024.15601.

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The recommendation system has been widely used in life, which greatly facilitates people's life. The traditional recommendation method is mainly used to analyze the interaction between users and items. analyze the history of users and items, and get only the users' preferences for items in the past. The serialization recommendation system analyzes the sequence of users interacting with objects in a recent period of time. To consider the relevance of the user's before and after behavior, can obtain the user's preference for items in the short term. However, the serialization method emphasizes t

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Amin, Ruhul, Taufik Djatna, Annisa Annisa, and Imas Sukaesih Sitanggang. "SKYLINE QUERY BASED ON USER PREFERENCES IN CELLULAR ENVIRONMENTS." JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) 9, no. 1 (2023): 143–53. http://dx.doi.org/10.33480/jitk.v9i1.4192.

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The recommendation system is an important tool for providing personalized suggestions to users about products or services. However, previous research on individual recommendation systems using skyline queries has not considered the dynamic personal preferences of users. Therefore, this study aims to develop an individual recommendation model based on the current individual preferences and user location in a mobile environment. We propose an RFM (Recency, Frequency, Monetary) score-based algorithm to predict the current individual preferences of users. This research utilizes the skyline query m

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Žnidaršič, Martin, Aljaž Osojnik, Peter Rupnik, and Bernard Ženko. "Improving Effectiveness of a Coaching System through Preference Learning." Technologies 10, no. 1 (2022): 24. http://dx.doi.org/10.3390/technologies10010024.

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The paper describes an approach for indirect data-based assessment and use of user preferences in an unobtrusive sensor-based coaching system with the aim of improving coaching effectiveness. The preference assessments are used to adapt the reasoning components of the coaching system in a way to better align with the preferences of its users. User preferences are learned based on data that describe user feedback as reported for different coaching messages that were received by the users. The preferences are not learned directly, but are assessed through a proxy—classifications or probabilities

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Wu, Li, and Ma. "A Comparative Study of Spatial and Temporal Preferences for Waterfronts in Wuhan based on Gender Differences in Check-In Behavior." ISPRS International Journal of Geo-Information 8, no. 9 (2019): 413. http://dx.doi.org/10.3390/ijgi8090413.

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The geographical location and check-in frequency of social platform users indicate their personal preferences and intentions for space. On the basis of social media data and gender differences, this study analyzes Weibo users’ preferences and the reasons behind these preferences for the waterfronts of the 21 major lakes within Wuhan’s Third Ring Road, in accordance with users’ check-in behaviors. According to the distribution characteristics of the waterfronts’ points of interest, this study explores the preferences of male and female users for waterfronts and reveals, through the check-in beh

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Thijssen, Kirsten, Marion Vlemminx, Michelle Westerhuis, Jeanne Dieleman, M. Beatrijs Van der Hout-Van der Jagt, and S. Guid Oei. "Uterine Monitoring Techniques from Patients' and Users' Perspectives." American Journal of Perinatology Reports 08, no. 03 (2018): e184-e191. http://dx.doi.org/10.1055/s-0038-1669409.

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Objective To evaluate preferences from patients and users on 3 uterine monitoring techniques, during labor. Study Design Women in term labor were simultaneously monitored with the intrauterine pressure catheter, the external tocodynamometer, and the electrohysterograph. Postpartum, these women filled out a questionnaire evaluating their preferences and important aspects. Nurses completed a questionnaire evaluating users' preferences. Results Of all 52 participating women, 80.8% preferred the electrohysterograph, 17.3% the intrauterine pressure catheter and 1.9% the external tocodynamometer. Fo

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Stroud, Laura, Erika Werner, Kristen Matteson, et al. "Waterpipe (hookah) tobacco use in pregnancy: use, preferences and perceptions of flavours." Tobacco Control 29, Suppl 2 (2019): s62—s71. http://dx.doi.org/10.1136/tobaccocontrol-2019-054984.

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ObjectiveWaterpipe tobacco (WPT; hookah) use is common in pregnant and reproductive-age women. Sweet flavours contribute to the appeal of WPT and are a potential regulatory target. This study investigated use, preferences and perceptions of WPT flavours in pregnant WPT users, and the impact of flavour preferences on preconception/prenatal WPT use and exposure biomarkers.Methods58 pregnant WPT users (mean age=27 years) completed a detailed interview regarding their WPT flavours use, preferences and perceptions. Biomarkers of nicotine and carcinogen exposure (eg, cotinine, benzene, butadiene) we

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Roy, Senjuti Basu, Baruch Schieber, and Nimrod Talmon. "Fairness in Preference Queries: Social Choice Theories Meet Data Management." Proceedings of the VLDB Endowment 17, no. 12 (2024): 4225–28. http://dx.doi.org/10.14778/3685800.3685841.

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Given a large number (notationally m ) of users' (members or voters) preferences as inputs over a large number of items or candidates (notationally n ), preference queries leverage different preference aggregation methods to aggregate individual preferences in a systematic manner and come up with a single output (either a complete order or top- k , ordered or unordered) that is most representative of the users' preferences. The goal of this 1.5 hour lecture style tutorial is to adapt different preference aggregation methods from social choice theories, summarize how existing research has handl

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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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Tian, Zhiqiang, Yezheng Liu, Jianshan Sun, Yuanchun Jiang, and Mingyue Zhu. "Exploiting Group Information for Personalized Recommendation with Graph Neural Networks." ACM Transactions on Information Systems 40, no. 2 (2022): 1–23. http://dx.doi.org/10.1145/3464764.

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Personalized recommendation has become more and more important for users to quickly find relevant items. The key issue of the recommender system is how to model user preferences. Previous work mostly employed user historical data to learn users’ preferences, but faced with the data sparsity problem. The prevalence of online social networks promotes increasing online discussion groups, and users in the same group often have similar interests and preferences. Therefore, it is necessary to integrate group information for personalized recommendation. The existing work on group-information-enhanced

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Braziunas, Darius, and Craig Boutilier. "Elicitation of Factored Utilities." AI Magazine 29, no. 4 (2008): 79. http://dx.doi.org/10.1609/aimag.v29i4.2203.

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The effective tailoring of decisions to the needs and desires of specific users requires automated mechanisms for preference assessment. We provide a brief overview of recent direct preference elicitation methods: these methods ask users to answer (ideally, a small number of) queries regarding their preferences and use this information to recommend a feasible decision that would be (approximately) optimal given those preferences. We argue for the importance of assessing numerical utilities rather than qualitative preferences, and survey several utility elicitation techniques from artificial in

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Zhou, Qingqing, and Chengzhi Zhang. "Detecting Users' Dietary Preferences and Their Evolutions via Chinese Social Media." Journal of Database Management 29, no. 3 (2018): 89–110. http://dx.doi.org/10.4018/jdm.2018070105.

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Dietary preferences are linked to human life and region culture. With the rapid development of the Internet, people are becoming frequently interested in sharing their opinions about dietary in social media. This article aims to mine social media users' dietary preferences and their evolutions with user generated content. The authors use microblogs from weibo.com to detect dietary preferences and their evolutions of social media users in China via sentiment analysis. First, the authors compare four aspect extraction methods to obtain dietary aspects. Second, sentiment polarities of aspects and

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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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Li, Nan, William Cushing, Subbarao Kambhampati, and Sungwook Yoon. "Learning User Plan Preferences Obfuscated by Feasibility Constraints." Proceedings of the International Conference on Automated Planning and Scheduling 19 (October 16, 2009): 370–73. http://dx.doi.org/10.1609/icaps.v19i1.13393.

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It has long been recognized that users can have complex preferences on plans. Non-intrusive learning of such preferences by observing the plans executed by the user is an attractive idea. Unfortunately, the executed plans are often not a true representation of user preferences, as they result from the interaction between user preferences and feasibility constraints. In the travel planning scenario, a user whose true preference is to travel by a plane may well be frequently observed traveling by car because of feasibility constraints (perhaps the user is a poor graduate student). In this work,

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Olteanu, Alexandra-Mihaela, Mathias Humbert, Kévin Huguenin, and Jean-Pierre Hubaux. "The (Co-)Location Sharing Game." Proceedings on Privacy Enhancing Technologies 2019, no. 2 (2019): 5–25. http://dx.doi.org/10.2478/popets-2019-0017.

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Abstract Most popular location-based social networks, such as Facebook and Foursquare, let their (mobile) users post location and co-location (involving other users) information. Such posts bring social benefits to the users who post them but also to their friends who view them. Yet, they also represent a severe threat to the users’ privacy, as co-location information introduces interdependences between users. We propose the first game-theoretic framework for analyzing the strategic behaviors, in terms of information sharing, of users of OSNs. To design parametric utility functions that are re

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Viappiani, P., B. Faltings, and P. Pu. "Preference-based Search using Example-Critiquing with Suggestions." Journal of Artificial Intelligence Research 27 (December 15, 2006): 465–503. http://dx.doi.org/10.1613/jair.2075.

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We consider interactive tools that help users search for their most preferred item in a large collection of options. In particular, we examine example-critiquing, a technique for enabling users to incrementally construct preference models by critiquing example options that are presented to them. We present novel techniques for improving the example-critiquing technology by adding suggestions to its displayed options. Such suggestions are calculated based on an analysis of users' current preference model and their potential hidden preferences. We evaluate the performance of our model-based sugg

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MATSUO, TOKURO, and TAKAYUKI FUJIMOTO. "A NEW LECTURE ALLOCATION SUPPORT SYSTEM BASED ON USERS' MULTIPLE PREFERENCES IN CAMPUS INFORMATION SYSTEMS." International Journal of Computational Intelligence and Applications 06, no. 02 (2006): 245–56. http://dx.doi.org/10.1142/s1469026806001964.

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This paper proposes an effective lecture allocation method based on users' profiles and utilities in elective subjects. In many universities and colleges, elective subject systems are employed as a curriculum in which students make their own learning experiences. Students select multiple subjects based on their interests and preferences. Generally, each university determines members of elective subjects based on simple rules, such as the order of grade, random selection, and first arrival. Current determination systems have strong limitations in terms of reflecting users' multiple preferences.

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Alshehri, Aziz, and Fayez Alotaibi. "Profiling Mobile Users Privacy Preferences." International Journal for Digital Society 10, no. 1 (2019): 1436–41. http://dx.doi.org/10.20533/ijds.2040.2570.2019.0178.

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Shi, Xiao Wei, Lin Ping Huang, Wei Jian Mi, Dao Fang Chang, and Yan Zhang. "A Personalized Music Recommender Based on Potential Preference Learning Dynamically." Advanced Engineering Forum 1 (September 2011): 395–99. http://dx.doi.org/10.4028/www.scientific.net/aef.1.395.

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An intelligent musical recommendation system for multi-users in network context is presented. The system is based on a comprehensive user profile described by feature-weight-like_degree-scene vectors. According different scenes, the system can filter the music that user may like in the internet, and form a music recommendation list which will be sent to the user. The Preference Learning Agent updates the users’ profile dynamically based on explicit feedback or the hidden preference obtained from the users’ behavior. The learning rate of like_degree, original like_degree and the weight of featu

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Liu, Chunyang, Chao Liu, Haiqiang Xin, Jian Wang, Jiping Liu, and Shenghua Xu. "Joint Geosequential Preference and Distance Metric Factorization for Point-of-Interest Recommendation." Mathematical Problems in Engineering 2020 (October 30, 2020): 1–14. http://dx.doi.org/10.1155/2020/6582676.

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Point-of-interest (POI) recommendation is a valuable service to help users discover attractive locations in location-based social networks (LBSNs). It focuses on capturing users’ movement patterns and location preferences by using massive historical check-in data. In the past decade, matrix factorization has become a mature and widely used technology in POI recommendation. However, the inner product of latent vectors adopted in matrix factorization methods does not satisfy the triangle inequality property, which may limit the expressiveness and lead to suboptimal solutions. Besides, the extrem

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Liu, Duen-Ren, Chuen-He Liou, Chi-Chieh Peng, and Huai-Chun Chi. "Hybrid content filtering and reputation-based popularity for recommending blog articles." Online Information Review 38, no. 6 (2014): 788–805. http://dx.doi.org/10.1108/oir-12-2013-0273.

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Purpose – Social bookmarking is a system which allows users to share, organise, search and manage bookmarks of web resources. However, with the rapid growth in the production of online documents, people are facing the problem of information overload. Social bookmarking web sites offer a solution to this by providing push counts, which are counts of users’ recommendations of articles, and thus indicate the popularity and interest thereof. In this way, users can use the push counts to find popular and interesting articles. A measure of popularity-based solely on push counts, however, cannot be c

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Wei, Feng, Shuyu Chen, Jie Jin, Shuai Zhang, Hongwei Zhou, and Yingbo Wu. "Adaptive Alleviation for Popularity Bias in Recommender Systems with Knowledge Graph." Security and Communication Networks 2022 (April 7, 2022): 1–9. http://dx.doi.org/10.1155/2022/4264489.

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Recommender systems are known to suffer from the popularity bias problem: popular items are recommended frequently, and nonpopular ones rarely, if at all. Prior studies focused on tackling this issue by increasing the number of recommended nonpopular (long-tail) items. However, these methods ignore the users’ personal popularity preferences and increase the exposure rate of the nonpopular items indiscriminately, which may hurt the user experience because different users have diverse interests in popularity. In this work, we propose a novel debias framework with knowledge graph (AWING), which a

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Shewale, Akshada, Kiran Patil, Amol Dhande, Karan Wanare, and Prof. A. D. Bhople. "A Review on Hotel Recommendation System Using Deep Learning & Dempster-Shafer Theory." International Journal of Ingenious Research, Invention and Development (IJIRID) 4, no. 1 (2025): 007–15. https://doi.org/10.5281/zenodo.14925915.

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<em>Context-aware recommender systems have been widely investigated in both academia and industry because they can make recommendations based on a user's current context (e.g., location, time). However, most existing context-aware techniques only use contextual information at the item level when modeling users' preferences, i.e., contextual information that correlates with users' overall evaluations of items such as ratings. Few studies have attempted to detect more finne-grained contextual preferences at the level of item aspects (e.g., a hotel's \ location, \ food quality, and \service). In

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Jun, Han Jong, Jae Hee Kim, Deuk Young Rhee, and Sun Woo Chang. "“SeoulHouse2Vec”: An Embedding-Based Collaborative Filtering Housing Recommender System for Analyzing Housing Preference." Sustainability 12, no. 17 (2020): 6964. http://dx.doi.org/10.3390/su12176964.

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Housing preference is the subjective and relative preference of users toward housing alternatives and studies in the field have been conducted to analyze the housing preferences of groups with sharing the same socio-demographic attributes. However, previous studies may not suggest the preference of individuals. In this regard, this study proposes “SeoulHouse2Vec,” an embedding-based collaborative filtering housing recommendation system for analyzing atypical and nonlinear housing preference of individuals. The model maps users and items in each dense vector space which are called embedding lay

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Telemala, Joseph P., and Hussein Suleman. "Exploring Topic-language Preferences in Multilingual Swahili Information Retrieval in Tanzania." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 6 (2021): 1–30. http://dx.doi.org/10.1145/3458671.

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Habitual switching of languages is a common behaviour among polyglots when searching for information on the Web. Studies in information retrieval (IR) and multilingual information retrieval (MLIR) suggest that part of the reason for such regular switching of languages is the topic of search. Unlike survey-based studies, this study uses query and click-through logs. It exploits the querying and results selection behaviour of Swahili MLIR system users to explore how topic of search (query) is associated with language preferences—topic-language preferences. This article is based on a carefully co

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Pu, Pearl, and Li Chen. "User-Involved Preference Elicitation for Product Search and Recommender Systems." AI Magazine 29, no. 4 (2008): 93. http://dx.doi.org/10.1609/aimag.v29i4.2200.

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We address user system interaction issues in product search and recommender systems: how to help users select the most preferential item from a large collection of alternatives. As such systems must crucially rely on an accurate and complete model of user preferences, the acquisition of this model becomes the central subject of our paper. Many tools used today do not satisfactorily assist users to establish this model because they do not adequately focus on fundamental decision objectives, help them reveal hidden preferences, revise conflicting preferences, or explicitly reason about tradeoffs

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Luo, Jianhong, Xuwei Pan, Shixiong Wang, and Yujing Huang. "Identifying target audience on enterprise social network." Industrial Management & Data Systems 119, no. 1 (2019): 111–28. http://dx.doi.org/10.1108/imds-01-2018-0007.

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Purpose Delivering messages and information to potentially interested users is one of the distinguishing applications of online enterprise social network (ESN). The purpose of this paper is to provide insights to better understand the repost preferences of users and provide personalized information service in enterprise social media marketing. Design/methodology/approach It is accomplished by constructing a target audience identification framework. Repost preference latent Dirichlet allocation (RPLDA) topic model topic model is proposed to understand the mass user online repost preferences tow

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Hammoodi, Mahmood Shakir, and Ahmed Al-Azawei. "A proposed approach to discover nearest users on social media networks based on users' profiles and preferences." Bulletin of Electrical Engineering and Informatics 12, no. 4 (2023): 2464–73. http://dx.doi.org/10.11591/beei.v12i4.4436.

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Social media sites (SMSs) become essential platforms used by people, customers, and companies for communication and marketing. Social media networks (SMNs) allow access to individuals who share their information and this, in turn, can lead to build users' profiles. Profiles can consist of a basic description of users' characteristics such as name, age, gender, education, marital status, email, phone number, and location. Preferences, on the other side, describe users' behavior on SMNs. Earlier literature defined a user's identity in different networks based on matching his/her name only. This

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Hammoodi, Mahmood Shakir, and Ahmed Al-Azawei. "A proposed approach to discover nearest users on social media networks based on users' profiles and preferences." Bulletin of Electrical Engineering and Informatics 12, no. 4 (2023): 2464–73. http://dx.doi.org/10.11591/eei.v12i4.4436.

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Social media sites (SMSs) become essential platforms used by people, customers, and companies for communication and marketing. Social media networks (SMNs) allow access to individuals who share their information and this, in turn, can lead to build users' profiles. Profiles can consist of a basic description of users' characteristics such as name, age, gender, education, marital status, email, phone number, and location. Preferences, on the other side, describe users' behavior on SMNs. Earlier literature defined a user's identity in different networks based on matching his/her name only. This

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Tewell, Eamon C. "Frequent Internet Users May Prefer More Health Care Information and Participation in Decision-Making." Evidence Based Library and Information Practice 9, no. 1 (2014): 51. http://dx.doi.org/10.18438/b8990n.

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Objective – To determine whether there is a significant relationship between patients’ frequency of Internet use and their health care information and decision-making preferences.  Design – Cross-sectional questionnaire survey.  Settings – Undergraduate classes at a large state university and senior-oriented computer classes at public libraries and senior centers.  Subjects – 438 respondents, including 226 undergraduates (mean age 20) and 212 community-dwelling older adults (mean age 72).   Methods – Respondents were administered the Health Infor

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Almu, Abba, Aliyu Ahmad, Abubakar Roko, and Mansur Aliyu. "Incorporating Preference Changes through Users’ Input in Collaborative Filtering Movie Recommender System." International Journal of Information Technology and Computer Science 14, no. 4 (2022): 48–56. http://dx.doi.org/10.5815/ijitcs.2022.04.05.

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The usefulness of Collaborative filtering recommender system is affected by its ability to capture users' preference changes on the recommended items during recommendation process. This makes it easy for the system to satisfy users' interest over time providing good and quality recommendations. The Existing system studied fails to solicit for user inputs on the recommended items and it is also unable to incorporate users' preference changes with time which lead to poor quality recommendations. In this work, an Enhanced Movie Recommender system that recommends movies to users is presented to im

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Bagus Hermawan, Aldi, Muhammad Yustitio Hadi Utomo, Pramudya Abimanyu, et al. "ANALYSIS OF INDONESIAN LANGUAGE PREFERENCES IN UPN "VETERAN" JAWA TIMUR STUDENTS AS WEBSITE-BASED INFORMATION SYSTEM USERS." Matapena: Jurnal Keilmuan Bahasa, Sastra, dan Pengajarannya 6, no. 1 (2023): 75–86. http://dx.doi.org/10.36815/matapena.v6i01.2639.

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This study aims to determine the preferences of Indonesian among UPN "Veteran" East Java students as users of a website-based information system, to determine the factors that influence the preferences of Indonesian students at UPN "Veteran" East Java as users of a website-based information system, and to find out the differences in Indonesian language preference for East Java "Veteran" UPN students as users of a website-based information system. This study used a quantitative research method by distributing questionnaires for data collection. The results of this study show that the preference

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Rigi, Mohammad Amin, and Farid Khoshalhan. "Eliciting User Preferences in Multi-Agent Meeting Scheduling Problem." International Journal of Intelligent Information Technologies 7, no. 2 (2011): 45–62. http://dx.doi.org/10.4018/jiit.2011040103.

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Meeting Scheduling Problem (MSP) arranges meetings between a number of participants. Reaching consensus in arranging a meeting is very diffuclt and time-consuming when the number of participants is large. One efficient approach for overcoming this problem is the use of multi-agent systems. In a multi-agent system, agents are deciding on behalf of their users. They must be able to elicite their users’ preferences in an effective way. This paper focuses on the elicitation of users’ preferences. Analytical hierarchy process (AHP) - which is known for its ability to determine preferences - is used

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Andreozzi, Sergio, Danilo Montesi, and Rocco Moretti. "XMatch: A Language for Satisfaction-Based Selection of Grid Services." Scientific Programming 13, no. 4 (2005): 299–316. http://dx.doi.org/10.1155/2005/294529.

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Grid systems enable the sharing of a large number of geographically-dispersed resources among different communities of users. They require a mapping functionality for the association of users requests expressed in terms of requirements and preferences to actual resources. This functionality should deal with a potentially high number of similar resources and with the diversity of the perceived satisfactions of users. We propose XMatch, a query language enabling the expression of the user request in terms of the expected satisfaction over XML-based representation of available resources. This lan

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Jeong, Soo-Yeon, and Young-Kuk Kim. "Deep Learning-Based Context-Aware Recommender System Considering Change in Preference." Electronics 12, no. 10 (2023): 2337. http://dx.doi.org/10.3390/electronics12102337.

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In order to predict and recommend what users want, users’ information is required, and more information is required to improve the performance of the recommender system. As IoT devices and smartphones have made it possible to know the user’s context, context-aware recommender systems have emerged to predict preferences by considering the user’s context. A context-aware recommender system uses contextual information such as time, weather, and location to predict preferences. However, a user’s preferences are not always the same in a given context. They may follow trends or make different choice

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Sun, Ke, Tieyun Qian, Tong Chen, Yile Liang, Quoc Viet Hung Nguyen, and Hongzhi Yin. "Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 214–21. http://dx.doi.org/10.1609/aaai.v34i01.5353.

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Point-of-Interest (POI) recommendation has been a trending research topic as it generates personalized suggestions on facilities for users from a large number of candidate venues. Since users' check-in records can be viewed as a long sequence, methods based on recurrent neural networks (RNNs) have recently shown promising applicability for this task. However, existing RNN-based methods either neglect users' long-term preferences or overlook the geographical relations among recently visited POIs when modeling users' short-term preferences, thus making the recommendation results unreliable. To a

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Peters, Dominik, and Ariel D. Procaccia. "Preference Elicitation as Average-Case Sorting." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 6 (2021): 5647–55. http://dx.doi.org/10.1609/aaai.v35i6.16709.

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Many decision making systems require users to indicate their preferences via a ranking. It is common to elicit such rankings through pairwise comparison queries. By using sorting algorithms, this can be achieved by asking at most O(m log m) adaptive comparison queries. However, in many cases we have some advance (probabilistic) information about the user's preferences, for instance if we have a learnt model of the user's preferences or if we expect the user's preferences to be correlated with those of previous users. For these cases, we design elicitation algorithms that ask fewer questions in

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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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Becher, Stefan, and Armin Gerl. "ConTra Preference Language: Privacy Preference Unification via Privacy Interfaces." Sensors 22, no. 14 (2022): 5428. http://dx.doi.org/10.3390/s22145428.

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After the enactment of the GDPR in 2018, many companies were forced to rethink their privacy management in order to comply with the new legal framework. These changes mostly affect the Controller to achieve GDPR-compliant privacy policies and management.However, measures to give users a better understanding of privacy, which is essential to generate legitimate interest in the Controller, are often skipped. We recommend addressing this issue by the usage of privacy preference languages, whereas users define rules regarding their preferences for privacy handling. In the literature, preference la

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Khadka, Salu, Pragya Shrestha Chaise, and Sujin Shrestha. "Restaurant Recommendation System Using User Based Collaborative Filtering." Asian Journal of Electrical Sciences 9, no. 2 (2021): 17–24. http://dx.doi.org/10.51983/ajes-2020.9.2.2552.

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A recommendation system is an application that can identify entities of interest for a person and provide suggestions based on the past record of person’s likes and preferences. The entity of interest can be anything, for example it can be a product, a movie or a news article. Recommender system is an effective way to help users to obtain the personalized and useful information. However, due to complexity and dynamic, the traditional recommender system cannot work well in mobile environment. Keeping such things into consideration, this recommendation system aims to recommend restaurants to use

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Ramon Hurdawaty and Kenny Dylun. "Consumer Preferences in Choosing Online Food Delivery Services in Jakarta." International Journal of Travel, Hospitality and Events 3, no. 2 (2024): 127–37. http://dx.doi.org/10.56743/ijothe.v3i2.370.

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Purpose: This research aims to determine consumer preference factors when purchasing Jakarta's online food delivery services. Research methods: This study uses a comparative quantitative method. Data was obtained by distributing online questionnaires via Google form to 100 users of Online Food Delivery Services (Grab-Food et al.) in Jakarta. In this study, the sample was determined by purposive sampling. Researchers conducted a one-way ANOVA test to see if there were differences in respondents' preferences. The preference factors used are transaction, price, and promotion. Results and discussi

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Tian, Yaru, Hua Peng, Xiaoxia Dong, Liwang Li, and Wenqi Zhu. "Consumers’ Brand Preferences for Infant Formula: A Grounded Theory Approach." Sustainability 14, no. 13 (2022): 7600. http://dx.doi.org/10.3390/su14137600.

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In recent years, the market pattern of infant formula in China has changed dramatically. The market share of domestic infant formula has exceeded that of imports. The essence of the market share change of domestic and foreign brands is the change of consumers’ brand preferences. To explore which factors affected consumers’ brand preferences, our study conducted a qualitative research method based on the grounded theory, through in-depth interviews with 60 mothers in the Beijing-Tianjin-Hebei region, systematically identifying the factors which affect consumers’ brand preferences for infant for

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Tang, Jian, Jun Yan, Lei Ji, et al. "Collaborative Users’ Brand Preference Mining across Multiple Domains from Implicit Feedbacks." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 477–82. http://dx.doi.org/10.1609/aaai.v25i1.7899.

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Advanced e-applications require comprehensive knowledge about their users’ preferences in order to provide accurate personalized services. In this paper, we propose to learn users’ preferences to product brands from their implicit feedbacks such as their searching and browsing behaviors in user Web browsing log data. The user brand preference learning problem is challenge since (1) the users’ implicit feedbacks are extremely sparse in various product domains; and (2) we can only observe positive feedbacks from users’ behaviors. In this paper, we propose a latent factor model to collaboratively

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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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Stach, Jens. "How memorable experiences influence brand preference." Qualitative Market Research: An International Journal 20, no. 4 (2017): 394–415. http://dx.doi.org/10.1108/qmr-03-2016-0023.

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Purpose This paper aims to illuminate mechanisms through which memorable experiences with brands create lasting preferences. It is based on the proposition that intense positive (negative) affective consumption in the consumer’s youth creates powerful imprints, which influence brand preference (distaste) throughout life. Design/methodology/approach Autobiographical memories with Nutella are retrieved from three different user groups, i.e. heavy-, light- and non-users. The retrieved memory narratives are analysed using conditioning theory, i.e. operant, classical or no conditioning are identifi

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Gupta, Shalini, and Veer Sain Dixit. "A Meta-Heuristic Algorithm Approximating Optimized Recommendations for E-Commerce Business Promotions." International Journal of Information Technology Project Management 11, no. 2 (2020): 23–49. http://dx.doi.org/10.4018/ijitpm.2020040103.

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To provide personalized services such as online-product recommendations, it is usually necessary to model clickstream behavior of users if implicit preferences are taken into account. To accomplish this, web log mining is a promising approach that mines clickstream sessions and depicts frequent sequential paths that a customer follows while browsing e-commerce websites. Strong attributes are identified from the navigation behavior of users. These attributes reflect absolute preference (AP) of the customer towards a product viewed. The preferences are obtained only for the products clicked. The

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Alshehri, Aziz, and Fayez Alotaibi. "Predicting Users Mobile App Privacy Preferences." Journal of Computer and Communications 07, no. 10 (2019): 147–56. http://dx.doi.org/10.4236/jcc.2019.710014.

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Kasztelan, Kamil, and Jakub Smołka. "Preferences of modern mobile app users." Journal of Computer Sciences Institute 23 (June 30, 2022): 71–76. http://dx.doi.org/10.35784/jcsi.2820.

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Each user group has its own preferences for mobile applications. A better app will increase the satisfaction of existing users and encourage new people to download it. People are used to it that it's hard to get rid of them. A survey was conducted in the Włodawa district in the first quarter of 2020, in which 150 random people took part. It has been noticed that life with a mobile device in hand has become a habit. Users more willingly and more often use the help of mobile devices during shopping while looking for product information and promotions. It has been observed that users pay more att

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