research-article

Learning to recommend with social trust ensemble

Authors:

Michael R. LyuAuthors Info & Claims

SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval

Pages 203 - 210

https://doi.org/10.1145/1571941.1571978

Published: 19 July 2009 Publication History

Abstract

As an indispensable technique in the field of Information Filtering, Recommender System has been well studied and developed both in academia and in industry recently. However, most of current recommender systems suffer the following problems: (1) The large-scale and sparse data of the user-item matrix seriously affect the recommendation quality. As a result, most of the recommender systems cannot easily deal with users who have made very few ratings. (2) The traditional recommender systems assume that all the users are independent and identically distributed; this assumption ignores the connections among users, which is not consistent with the real world recommendations. Aiming at modeling recommender systems more accurately and realistically, we propose a novel probabilistic factor analysis framework, which naturally fuses the users' tastes and their trusted friends' favors together. In this framework, we coin the term Social Trust Ensemble to represent the formulation of the social trust restrictions on the recommender systems. The complexity analysis indicates that our approach can be applied to very large datasets since it scales linearly with the number of observations, while the experimental results show that our method performs better than the state-of-the-art approaches.

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Cited By

Xiong FNi XPan SChen HWang LYan Z(2025)Nonlinear Matrix Factorization With Cognitive Opinion Formation for Social RecommendationIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2024.351287955:3(1870-1885)Online publication date: Mar-2025
https://doi.org/10.1109/TSMC.2024.3512879
Heshmati AMeghdadi MAfsharchi MAhmadian S(2025)SiSRS: Signed social recommender system using deep neural network representation learningExpert Systems with Applications10.1016/j.eswa.2024.125205259(125205)Online publication date: Jan-2025
https://doi.org/10.1016/j.eswa.2024.125205
Chen PZhou WChang YZeng JWen J(2025)Multi-view collaborative signal fusion and representation property optimization for recommendationEngineering Applications of Artificial Intelligence10.1016/j.engappai.2025.110085144(110085)Online publication date: Mar-2025
https://doi.org/10.1016/j.engappai.2025.110085
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Index Terms

Learning to recommend with social trust ensemble
1. Applied computing
  1. Law, social and behavioral sciences
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

cover image ACM Conferences

SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval

July 2009

896 pages

ISBN:9781605584836

DOI:10.1145/1571941

General Chairs:
James Allan
University of Massachusetts Amherst, USA
,
Javed Aslam
Northeastern University, USA
,
Program Chairs:
Mark Sanderson
University of Sheffield, UK
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Justin Zobel
University of Melbourne, Australia

Copyright © 2009 ACM.

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: 19 July 2009

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SIGIR '09

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SIGIR '09: The 32nd International ACM SIGIR conference on research and development in Information Retrieval

July 19 - 23, 2009

MA, Boston, USA

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Xiong FNi XPan SChen HWang LYan Z(2025)Nonlinear Matrix Factorization With Cognitive Opinion Formation for Social RecommendationIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2024.351287955:3(1870-1885)Online publication date: Mar-2025
https://doi.org/10.1109/TSMC.2024.3512879
Heshmati AMeghdadi MAfsharchi MAhmadian S(2025)SiSRS: Signed social recommender system using deep neural network representation learningExpert Systems with Applications10.1016/j.eswa.2024.125205259(125205)Online publication date: Jan-2025
https://doi.org/10.1016/j.eswa.2024.125205
Chen PZhou WChang YZeng JWen J(2025)Multi-view collaborative signal fusion and representation property optimization for recommendationEngineering Applications of Artificial Intelligence10.1016/j.engappai.2025.110085144(110085)Online publication date: Mar-2025
https://doi.org/10.1016/j.engappai.2025.110085
Rehman AAhmed AAlahmadi TMirdad AAl Ghofaily BSaleem K(2024)A framework for generating recommendations based on trust in an informal e-learning environmentPeerJ Computer Science10.7717/peerj-cs.238610(e2386)Online publication date: 31-Oct-2024
https://doi.org/10.7717/peerj-cs.2386
Qiu YDong HChen JHe X(2024)LightAD: accelerating AutoDebias with adaptive samplingJUSTC10.52396/JUSTC-2022-010054:4(0405)Online publication date: 2024
https://doi.org/10.52396/JUSTC-2022-0100
Li MLi JYang LDing Q(2024)Self-Supervised Hypergraph Learning for Knowledge-Aware Social RecommendationElectronics10.3390/electronics1307130613:7(1306)Online publication date: 31-Mar-2024
https://doi.org/10.3390/electronics13071306
Lu HChen Z(2024)SocialJGCF: Social Recommendation with Jacobi Polynomial-Based Graph Collaborative FilteringApplied Sciences10.3390/app14241207014:24(12070)Online publication date: 23-Dec-2024
https://doi.org/10.3390/app142412070
Wang LZhang JYang HChen ZTang JZhang ZChen XLin YSun HSong RZhao WXu JDou ZWang JWen J(2024)User Behavior Simulation with Large Language Model-based Agents for Recommender SystemsACM Transactions on Information Systems10.1145/3708985Online publication date: 20-Dec-2024
https://dl.acm.org/doi/10.1145/3708985
Wang SFan WWei XMei XLin SLi Q(2024)Multi-Agent Attacks for Black-Box Social RecommendationsACM Transactions on Information Systems10.1145/369610543:1(1-26)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3696105
Li MLiu KLiu HZhao ZWard TWu X(2024)Heterogeneous Meta-Path Graph Learning for Higher-Order Social RecommendationACM Transactions on Knowledge Discovery from Data10.1145/367365818:8(1-25)Online publication date: 15-Jun-2024
https://dl.acm.org/doi/10.1145/3673658
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