research-article

Recommendation based on weighted social trusts and item relationships

Authors:

Lei GuoAuthors Info & Claims

SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing

Pages 254 - 259

https://doi.org/10.1145/2554850.2554884

Published: 24 March 2014 Publication History

Abstract

Recommender systems have been well studied to overcome the information overload problem in both academia and industry during the past decade. Collaborative filtering is the most popular approach in recommender systems, among which Probabilistic Matrix Factorization (PMF) has proved very effective. In social networks, users linked together tend to have similar interests and some social-based methods emerged based on this assumption. However these methods treat different friends equally and actually different people may have different influence on a person. Besides social trusts, item relationships also provide rich information especially when trust information is not sufficient. The items liked by the same user may have similar characteristics and whether an item is preferred is influenced by its similar items. But most of these methods ignore the role of item relationships. In this paper, we propose a novel recommendation method which incorporates social trusts and item relationships into PMF. We compute user influence degrees and item similarities to weight user-user and item-item relationships when learning user and item latent factors. Experimental results on Epinions dataset demonstrate that our method has shown a significant improvement over existing approaches especially for the users who have made few ratings.

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Cited By

Wang YSong JLou J(2019)A Trusted Measurement Model for Mobile InternetTrusted Computing and Information Security10.1007/978-981-13-5913-2_14(220-238)Online publication date: 9-Jan-2019
https://doi.org/10.1007/978-981-13-5913-2_14
Dakhel AMalazi HMahdavi M(2018)A social recommender system using item asymmetric correlationApplied Intelligence10.1007/s10489-017-0973-548:3(527-540)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1007/s10489-017-0973-5
Zhai HLi J(2015)Refine Social Relations and Differentiate the Same Friends' Influence in Recommender SystemProceedings of the Third International Conference on Mining Intelligence and Knowledge Exploration - Volume 946810.1007/978-3-319-26832-3_47(504-514)Online publication date: 9-Dec-2015
https://dl.acm.org/doi/10.1007/978-3-319-26832-3_47
Show More Cited By

Index Terms

Recommendation based on weighted social trusts and item relationships
1. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing

March 2014

1890 pages

ISBN:9781450324694

DOI:10.1145/2554850

Conference Chairs:
Yookun Cho
Seoul National University, Korea
,
Sung Y. Shin
South Dakota State University
,
Program Chairs:
Sangwook Kim
Kyungpook National University, Korea
,
Chih-Cheng Hung
Southern Polytechnic State University
,
Jiman Hong
Soongsil University, South Korea

Copyright © 2014 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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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 March 2014

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Funding Sources

Natural Science Foundation of Shandong Province
Ministry of Education of the People's Republic of China
National Natural Science Foundation of China

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SAC 2014

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SIGAPP

SAC 2014: Symposium on Applied Computing

March 24 - 28, 2014

Gyeongju, Republic of Korea

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SAC '14 Paper Acceptance Rate 218 of 939 submissions, 23%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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SAC '25

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sigapp

The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

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Cited By

Wang YSong JLou J(2019)A Trusted Measurement Model for Mobile InternetTrusted Computing and Information Security10.1007/978-981-13-5913-2_14(220-238)Online publication date: 9-Jan-2019
https://doi.org/10.1007/978-981-13-5913-2_14
Dakhel AMalazi HMahdavi M(2018)A social recommender system using item asymmetric correlationApplied Intelligence10.1007/s10489-017-0973-548:3(527-540)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1007/s10489-017-0973-5
Zhai HLi J(2015)Refine Social Relations and Differentiate the Same Friends' Influence in Recommender SystemProceedings of the Third International Conference on Mining Intelligence and Knowledge Exploration - Volume 946810.1007/978-3-319-26832-3_47(504-514)Online publication date: 9-Dec-2015
https://dl.acm.org/doi/10.1007/978-3-319-26832-3_47
Sun Z(2015)Exploiting Item and User Relationships for Recommender SystemsUser Modeling, Adaptation and Personalization10.1007/978-3-319-20267-9_37(397-402)Online publication date: 11-Jun-2015
https://doi.org/10.1007/978-3-319-20267-9_37
Sun ZGuo GZhang J(2015)Exploiting Implicit Item Relationships for Recommender SystemsUser Modeling, Adaptation and Personalization10.1007/978-3-319-20267-9_21(252-264)Online publication date: 11-Jun-2015
https://doi.org/10.1007/978-3-319-20267-9_21

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