research-article

Recommender systems with social regularization

Authors:

Michael R. Lyu,

Irwin KingAuthors Info & Claims

WSDM '11: Proceedings of the fourth ACM international conference on Web search and data mining

Pages 287 - 296

https://doi.org/10.1145/1935826.1935877

Published: 09 February 2011 Publication History

Abstract

Although Recommender Systems have been comprehensively analyzed in the past decade, the study of social-based recommender systems just started. In this paper, aiming at providing a general method for improving recommender systems by incorporating social network information, we propose a matrix factorization framework with social regularization. The contributions of this paper are four-fold: (1) We elaborate how social network information can benefit recommender systems; (2) We interpret the differences between social-based recommender systems and trust-aware recommender systems; (3) We coin the term Social Regularization to represent the social constraints on recommender systems, and we systematically illustrate how to design a matrix factorization objective function with social regularization; and (4) The proposed method is quite general, which can be easily extended to incorporate other contextual information, like social tags, etc. The empirical analysis on two large datasets demonstrates that our approaches outperform other state-of-the-art methods.

Supplementary Material

JPG File (wsdm2011_lyu_rss_01.jpg)

Download
12.38 KB

MP4 File (wsdm2011_lyu_rss_01.mp4)

Download
174.98 MB

References

[1]

D. Agarwal and B.-C. Chen. flda: matrix factorization through latent dirichlet allocation. In Proc. of WSDM '10, pages 91--100, New York, New York, USA, 2010.

Digital Library

[2]

P. Bedi, H. Kaur, and S. Marwaha. Trust based recommender system for semantic web. In Proc. of IJCAI'07, pages 2677--2682, 2007.

Digital Library

[3]

R. Bell, Y. Koren, and C. Volinsky. Modeling relationships at multiple scales to improve accuracy of large recommender systems. In Proc. of KDD '07, pages 95--104, San Jose, California, USA, 2007.

Digital Library

[4]

J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proc. of UAI '98, 1998.

Digital Library

[5]

J. Canny. Collaborative filtering with privacy via factor analysis. In Proc. of SIGIR '02, pages 238--245, Tampere, Finland, 2002.

Digital Library

[6]

M. Deshpande and G. Karypis. Item-based top-n recommendation. ACM Transactions on Information Systems, 22(1):143--177, 2004.

Digital Library

[7]

I. Guy, N. Zwerdling, D. Carmel, I. Ronen, E. Uziel, S. Yogev, and S. Ofek-Koifman. Personalized recommendation of social software items based on social relations. In Proc. of RecSys '09, pages 53--60, New York, New York, USA, 2009.

Digital Library

[8]

T. Hofmann. Collaborative filtering via gaussian probabilistic latent semantic analysis. In Proc. of SIGIR '03, pages 259--266, Toronto, Canada, 2003.

Digital Library

[9]

T. Hofmann. Latent semantic models for collaborative filtering. ACM Transactions on Information Systems, 22(1):89--115, 2004.

Digital Library

[10]

Z. Huang, H. Chen, and D. Zeng. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans. Inf. Syst., 22(1):116--142, 2004.

Digital Library

[11]

M. Jahrer, A. Töscher, and R. Legenstein. Combining predictions for accurate recommender systems. In Proc. of KDD '10, pages 693--702, Washington, DC, USA, 2010.

Digital Library

[12]

R. Jin, J. Y. Chai, and L. Si. An automatic weighting scheme for collaborative filtering. In Proc. of SIGIR '04, pages 337--344, Sheffield, United Kingdom, 2004.

Digital Library

[13]

A. Kohrs and B. Merialdo. Clustering for collaborative filtering applications. In Proceedings of CIMCA, 1999.

[14]

Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proc. of KDD '08, pages 426--434, Las Vegas, Nevada, USA, 2008.

Digital Library

[15]

Y. Koren. Collaborative filtering with temporal dynamics. In Proc. of KDD '09, pages 447--456, Paris, France, 2009.

Digital Library

[16]

D. D. Lee and H. S. Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755):788--791, Oct. 1999.

[17]

G. Linden, B. Smith, and J. York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, pages 76--80, Jan/Feb 2003.

Digital Library

[18]

F. Liu and H. J. Lee. Use of social network information to enhance collaborative filtering performance. Expert Syst. Appl., 37(7):4772--4778, 2010.

Digital Library

[19]

N. N. Liu and Q. Yang. Eigenrank: a ranking-oriented approach to collaborative filtering. In Proc. of SIGIR '08, pages 83--90, Singapore, Singapore, 2008.

Digital Library

[20]

Y. Lu, P. Tsaparas, A. Ntoulas, and L. Polanyi. Exploiting social context for review quality prediction. In Proc. of WWW '10, pages 691--700, Raleigh, North Carolina, USA, 2010.

Digital Library

[21]

H. Ma, I. King, and M. R. Lyu. Effective missing data prediction for collaborative filtering. In Proc. of SIGIR '07, pages 39--46, Amsterdam, The Netherlands, 2007.

Digital Library

[22]

H. Ma, I. King, and M. R. Lyu. Learning to recommend with social trust ensemble. In Proc. of SIGIR '09, pages 203--210, Boston, MA, USA, 2009.

Digital Library

[23]

H. Ma, H. Yang, M. R. Lyu, and I. King. SoRec: Social recommendation using probabilistic matrix factorization. In Proc. of CIKM '08, pages 931--940, Napa Valley, USA, 2008.

Digital Library

[24]

P. Massa and P. Avesani. Trust-aware collaborative filtering for recommender systems. In Proceedings of CoopIS/DOA/ODBASE, pages 492--508, 2004.

Digital Library

[25]

P. Massa and P. Avesani. Trust-aware recommender systems. In Proc. of RecSys '07, pages 17--24, Minneapolis, MN, USA, 2007.

Digital Library

[26]

Q. Mei, D. Cai, D. Zhang, and C. Zhai. Topic modeling with network regularization. In Proc. of WWW '08, pages 101--110, Beijing, China, 2008.

Digital Library

[27]

J. O'Donovan and B. Smyth. Trust in recommender systems. In Proc. of IUI '05, pages 167--174, San Diego, California, USA, 2005.

Digital Library

[28]

J. D. M. Rennie and N. Srebro. Fast maximum margin matrix factorization for collaborative prediction. In Proc. of ICML '05, 2005.

Digital Library

[29]

P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: An open architecture for collaborative filtering of netnews. In Proc. of CSCW '94, 1994.

Digital Library

[30]

R. Salakhutdinov and A. Mnih. Bayesian probabilistic matrix factorization using markov chain monte carlo. In Proc. of ICML '08, 2008.

Digital Library

[31]

R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In Advances in Neural Information Processing Systems, volume 20, 2008.

Digital Library

[32]

B. Sarwar, G. Karypis, J. Konstan, and J. Reidl. Item-based collaborative filtering recommendation algorithms. In Proc. of WWW '01, pages 285--295, Hong Kong, Hong Kong, 2001.

Digital Library

[33]

L. Si and R. Jin. Flexible mixture model for collaborative filtering. In Proc. of ICML '03, 2003.

[34]

N. Srebro and T. Jaakkola. Weighted low-rank approximations. In Proc. of ICML '03, pages 720--727, 2003.

[35]

N. Srebro, J. D. M. Rennie, and T. Jaakkola. Maximum-margin matrix factorization. In NIPS, 2004.

Digital Library

[36]

J. Wang, A. P. de Vries, and M. J. T. Reinders. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In Proc. of SIGIR '06, pages 501--508, Seattle, Washington, USA, 2006.

Digital Library

[37]

X. Xin, I. King, H. Deng, and M. R. Lyu. A social recommendation framework based on multi-scale continuous conditional random fields. In Proc. of CIKM '09, pages 1247--1256, Hong Kong, China, 2009.

Digital Library

[38]

G.-R. Xue, C. Lin, Q. Yang, W. Xi, H.-J. Zeng, Y. Yu, and Z. Chen. Scalable collaborative filtering using cluster-based smoothing. In Proc. of SIGIR '05, pages 114--121, Salvador, Brazil, 2005.

Digital Library

[39]

K. Yu, S. Zhu, J. Lafferty, and Y. Gong. Fast nonparametric matrix factorization for large-scale collaborative filtering. In Proc. of SIGIR '09, pages 211--218, Boston, MA, USA, 2009.

Digital Library

[40]

Y. Zhang and J. Koren. Efficient bayesian hierarchical user modeling for recommendation system. In Proc. of SIGIR '07, pages 47--54, Amsterdam, The Netherlands, 2007.

Digital Library

[41]

S. Zhu, K. Yu, and Y. Gong. Stochastic relational models for large-scale dyadic data using mcmc. In Advances in Neural Information Processing Systems, volume 21, 2009.

Cited By

Zhang HZhou XShen ZLi Y(2025)PrivFR: Privacy-Enhanced Federated Recommendation With Shared Hash EmbeddingIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.338775736:1(32-46)Online publication date: Jan-2025
https://doi.org/10.1109/TNNLS.2024.3387757
Hu ZNakagawa SZhuang YDeng JCai SZhou TRen F(2025)Hierarchical Denoising for Robust Social RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.350877837:2(739-753)Online publication date: Feb-2025
https://doi.org/10.1109/TKDE.2024.3508778
Wang LXu MZhang QShi YWu Q(2025)Causal disentanglement for regulating social influence bias in social recommendationNeurocomputing10.1016/j.neucom.2024.129133618(129133)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.129133
Show More Cited By

Index Terms

Recommender systems with social regularization
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

Recommendations

Improving Recommender Systems by Incorporating Social Contextual Information

Due to their potential commercial value and the associated great research challenges, recommender systems have been extensively studied by both academia and industry recently. However, the data sparsity problem of the involved user-item matrix seriously ...
Learning from Sets of Items in Recommender Systems

Most of the existing recommender systems use the ratings provided by users on individual items. An additional source of preference information is to use the ratings that users provide on sets of items. The advantages of using preferences on sets are ...
Differentiating Regularization Weights -- A Simple Mechanism to Alleviate Cold Start in Recommender Systems

Matrix factorization (MF) and its extended methodologies have been studied extensively in the community of recommender systems in the last decade. Essentially, MF attempts to search for low-ranked matrices that can (1) best approximate the known rating ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

WSDM '11: Proceedings of the fourth ACM international conference on Web search and data mining

February 2011

870 pages

ISBN:9781450304931

DOI:10.1145/1935826

General Chair:
Irwin King
CUHK, Hong Kong
,
Program Chairs:
Wolfgang Nejdl
L3S and University of Hannover, Germany
,
Hang Li
Microsoft Research Asia, China

Copyright © 2011 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 February 2011

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

WSDM'11

Sponsor:

WSDM'11: Fourth ACM International Conference on Web Search and Data Mining

February 9 - 12, 2011

Hong Kong, China

Acceptance Rates

WSDM '11 Paper Acceptance Rate 83 of 372 submissions, 22%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1,157
Total Citations
View Citations
6,271
Total Downloads

Downloads (Last 12 months)227
Downloads (Last 6 weeks)27

Reflects downloads up to 13 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zhang HZhou XShen ZLi Y(2025)PrivFR: Privacy-Enhanced Federated Recommendation With Shared Hash EmbeddingIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.338775736:1(32-46)Online publication date: Jan-2025
https://doi.org/10.1109/TNNLS.2024.3387757
Hu ZNakagawa SZhuang YDeng JCai SZhou TRen F(2025)Hierarchical Denoising for Robust Social RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.350877837:2(739-753)Online publication date: Feb-2025
https://doi.org/10.1109/TKDE.2024.3508778
Wang LXu MZhang QShi YWu Q(2025)Causal disentanglement for regulating social influence bias in social recommendationNeurocomputing10.1016/j.neucom.2024.129133618(129133)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.129133
Xu ZLi BCao W(2025)Enhancing federated learning-based social recommendations with graph attention networksNeurocomputing10.1016/j.neucom.2024.129045617(129045)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.129045
Aljunid MD.H. MHooshmand MAli WShetty AAlzoubah S(2025)A collaborative filtering recommender systems: SurveyNeurocomputing10.1016/j.neucom.2024.128718617(128718)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.128718
Meydani EDuesing CTrier M(2025)Modeling higher-order social influence using multi-head graph attention autoencoderInformation Systems10.1016/j.is.2024.102474128(102474)Online publication date: Feb-2025
https://doi.org/10.1016/j.is.2024.102474
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
Zhao HZhong JLiang XXie CWang S(2025)Application of machine learning in drug side effect prediction: databases, methods, and challengesFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-024-31063-019:5Online publication date: 1-May-2025
https://dl.acm.org/doi/10.1007/s11704-024-31063-0
Munson JCummins BZosso D(2025)An introduction to collaborative filtering through the lens of the Netflix PrizeKnowledge and Information Systems10.1007/s10115-024-02315-zOnline publication date: 10-Jan-2025
https://doi.org/10.1007/s10115-024-02315-z
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
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents