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Preference-oriented Social Networks: Group Recommendation and Inference

Published: 16 September 2015 Publication History

Abstract

Social networks facilitate a variety of social, economic, and political interactions. Homophily---the tendency for people to associate or interact with similar peers---and social influence---the tendency to adopt certain characteristics of those with whom one interacts---suggest that preferences (e.g., over products, services, political parties) are likely to be correlated among people whom directly interact in a social network. We develop a model, preference-oriented social networks, that captures such correlations of individual preferences, where preferences take the form of rankings over a set of options. We develop probabilistic inference methods for predicting individual preferences given observed social connections and partial observations of the preferences of others in the network. We exploit these predictions in a social choice context to make group decisions or recommendations even when the preferences of some group members are unobserved. Experiments demonstrate the effectiveness of our algorithms and the improvements made possible by accounting for social ties.

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cover image ACM Conferences
RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems
September 2015
414 pages
ISBN:9781450336925
DOI:10.1145/2792838
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 16 September 2015

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Author Tags

  1. group recommendation
  2. preferences
  3. probabilistic inference
  4. probabilistic models
  5. social networks

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  • Research-article

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  • Natural Sciences Engineering Research Council (NSERC)

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RecSys '15
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RecSys '15: Ninth ACM Conference on Recommender Systems
September 16 - 20, 2015
Vienna, Austria

Acceptance Rates

RecSys '15 Paper Acceptance Rate 28 of 131 submissions, 21%;
Overall Acceptance Rate 254 of 1,295 submissions, 20%

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  • (2023)Citizen-Centric Multiagent SystemsProceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems10.5555/3545946.3598843(1802-1807)Online publication date: 30-May-2023
  • (2023)Group recommendation exploiting characteristics of user-item and collaborative rating of usersMultimedia Tools and Applications10.1007/s11042-023-16799-483:10(29289-29309)Online publication date: 12-Sep-2023
  • (2023)Content Based Network Representational Learning for Movie Recommendation (CNMovieRec)Multi-disciplinary Trends in Artificial Intelligence10.1007/978-3-031-36402-0_10(112-123)Online publication date: 24-Jun-2023
  • (2022)Structural Balance Considerations for Networks with Preference Orders as Node Attributes2022 56th Asilomar Conference on Signals, Systems, and Computers10.1109/IEEECONF56349.2022.10051969(1255-1261)Online publication date: 31-Oct-2022
  • (2022)Influence-Based Deep Network for Next POIs PredictionAdvances in Information Retrieval10.1007/978-3-030-99736-6_12(170-183)Online publication date: 5-Apr-2022
  • (2021)Evaluating Recommender SystemsInternational Journal of Intelligent Information Technologies10.4018/ijiit.202104010217:2(25-45)Online publication date: Apr-2021
  • (2021)DeepGroup: Group Recommendation with Implicit FeedbackProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482081(3408-3412)Online publication date: 26-Oct-2021
  • (2021)Hierarchical Hyperedge Embedding-Based Representation Learning for Group RecommendationACM Transactions on Information Systems10.1145/345794940:1(1-27)Online publication date: 8-Sep-2021
  • (2021)Social-Enhanced Attentive Group RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.293647533:3(1195-1209)Online publication date: 1-Mar-2021
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