research-article

Multi-relational matrix factorization using bayesian personalized ranking for social network data

Authors:

Artus Krohn-Grimberghe,

Christoph Freudenthaler,

Lars Schmidt-ThiemeAuthors Info & Claims

WSDM '12: Proceedings of the fifth ACM international conference on Web search and data mining

Pages 173 - 182

https://doi.org/10.1145/2124295.2124317

Published: 08 February 2012 Publication History

Abstract

A key element of the social networks on the internet such as Facebook and Flickr is that they encourage users to create connections between themselves, other users and objects.

One important task that has been approached in the literature that deals with such data is to use social graphs to predict user behavior (e.g. joining a group of interest). More specifically, we study the cold-start problem, where users only participate in some relations, which we will call social relations, but not in the relation on which the predictions are made, which we will refer to as target relations.

We propose a formalization of the problem and a principled approach to it based on multi-relational factorization techniques. Furthermore, we derive a principled feature extraction scheme from the social data to extract predictors for a classifier on the target relation. Experiments conducted on real world datasets show that our approach outperforms current methods.

References

[1]

Z. Gantner, L. Drumond, C. Freudenthaler, S. Rendle, and L. Schmidt-Thieme. Learning attribute-to-feature mappings for cold-start recommendations. In Proceedings of the 2010 IEEE International Conference on Data Mining, ICDM '10, pages 176--185, Washington, DC, USA, 2010. IEEE Computer Society.

Digital Library

[2]

Z. Gantner, S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. Mymedialite: A free recommender system library. In Proceedings of the 5th ACM International Conference on Recommender Systems (RecSys 2011) (to appear), 2011.

Digital Library

[3]

M. Jamali and M. Ester. A matrix factorization technique with trust propagation for recommendation in social networks. In Proceedings of the fourth ACM conference on Recommender systems, RecSys '10, pages 135--142, New York, NY, USA, 2010. ACM.

Digital Library

[4]

Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8):30--37, 2009.

Digital Library

[5]

H. Ma, I. King, and M. R. Lyu. Learning to recommend with social trust ensemble. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, SIGIR '09, pages 203--210, New York, NY, USA, 2009. ACM.

Digital Library

[6]

H. Ma, H. Yang, M. R. Lyu, and I. King. Sorec: social recommendation using probabilistic matrix factorization. In Proceeding of the 17th ACM conference on Information and knowledge management, CIKM '08, pages 931--940, New York, NY, USA, 2008. ACM.

Digital Library

[7]

D. R. Musicant, V. Kumar, and A. Ozgur. Optimizing f-measure with support vector machines. In Proceedings of the Sixteenth International Florida Artificial Intelligence Research Society Conference, pages 356--360. Haller AAAI Press, 2003.

[8]

M. E. J. Newman. Modularity and community structure in networks. Proc. Natl. Acad. Sci. USA, 103:8577--8582, 2006.

[9]

S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI '09, pages 452--461, Arlington, Virginia, United States, 2009. AUAI Press.

Digital Library

[10]

S. Rendle and L. Schmidt-Thieme. Pairwise interaction tensor factorization for personalized tag recommendation. In Proceedings of the third ACM international conference on Web search and data mining, WSDM '10, pages 81--90, New York, NY, USA, 2010. ACM.

Digital Library

[11]

A. I. Schein, A. Popescul, L. H. Ungar, and D. M. Pennock. Methods and metrics for cold-start recommendations. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '02, pages 253--260, New York, NY, USA, 2002. ACM.

Digital Library

[12]

A. P. Singh and G. J. Gordon. Relational learning via collective matrix factorization. In Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '08, pages 650--658, New York, NY, USA, 2008. ACM.

Digital Library

[13]

A. P. Singh and G. J. Gordon. A unified view of matrix factorization models. In Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II, ECML PKDD '08, pages 358--373, Berlin, Heidelberg, 2008. Springer-Verlag.

Digital Library

[14]

L. Tang and H. Liu. Relational learning via latent social dimensions. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '09, pages 817--826, New York, NY, USA, 2009. ACM.

Digital Library

[15]

L. Tang and H. Liu. Scalable learning of collective behavior based on sparse social dimensions. In Proceeding of the 18th ACM conference on Information and knowledge management, CIKM '09, pages 1107--1116, New York, NY, USA, 2009. ACM.

Digital Library

[16]

Yang, Long, Smola, Sadagopan, Zheng, and Zha. Like like alike - joint friendship and interest propagation in social networks. In Proceedings of the WWW 2011, 2011.

Digital Library

[17]

Y. Zhang, B. Cao, and D.-Y. Yeung. Multi-domain collaborative filtering. In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI), pages 725--732, Catalina Island, California, 2010.

Cited By

Cheng ZDong JLiu FZhu LYang XWang M(2024)Disentangled Cascaded Graph Convolution Networks for Multi-Behavior RecommendationACM Transactions on Recommender Systems10.1145/36732442:4(1-27)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3673244
Lee SKo GSong HJung JSerra ESpezzano F(2024)MuLe: Multi-Grained Graph Learning for Multi-Behavior RecommendationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679709(1163-1173)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679709
Bing RYuan GCai ZLi BZhou Y(2024)Automated Modeling of Influence Diversity with Graph Convolutional Network for Social RecommendationWeb and Big Data10.1007/978-981-97-7235-3_3(33-49)Online publication date: 28-Aug-2024
https://doi.org/10.1007/978-981-97-7235-3_3
Show More Cited By

Index Terms

Multi-relational matrix factorization using bayesian personalized ranking for social network data
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

Recommendations

Collaborative Topic Regression with social trust ensemble for recommendation in social media systems

Social media systems provide ever-growing huge volumes of information for dissemination and communication among communities of users, while recommender systems aim to mitigate information overload by filtering and providing users the most attractive and ...
Heterogeneous data fusion via matrix factorization for augmenting item, group and friend recommendations
SAC '13: Proceedings of the 28th Annual ACM Symposium on Applied Computing

Up to now, more and more social media sites have started to allow their users to build the social relationships. Take the Last.fm for example (which is a popular music-sharing site), users can not only add each other as friends, but also join interest ...
Multi-linear interactive matrix factorization

A multi-linear interactive matrix factorization algorithm is introduced.The interactions between users and factors are empirically analyzed.Results show interactive factors significantly enhance recommendation performance. Recommender systems, which can ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

WSDM '12: Proceedings of the fifth ACM international conference on Web search and data mining

February 2012

792 pages

ISBN:9781450307475

DOI:10.1145/2124295

General Chairs:
Eytan Adar
University of Michigan, USA
,
Jaime Teevan
Microsoft Research, USA
,
Program Chairs:
Eugene Agichtein
Emory University, USA
,
Yoelle Maarek
Yahoo! Research, Israel

Copyright © 2012 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: 08 February 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

WSDM'12

Sponsor:

WSDM'12: Fifth ACM International Conference on Web Search and Data Mining

February 8 - 12, 2012

Washington, Seattle, USA

Acceptance Rates

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

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

138
Total Citations
View Citations
1,248
Total Downloads

Downloads (Last 12 months)20
Downloads (Last 6 weeks)3

Reflects downloads up to 21 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Cheng ZDong JLiu FZhu LYang XWang M(2024)Disentangled Cascaded Graph Convolution Networks for Multi-Behavior RecommendationACM Transactions on Recommender Systems10.1145/36732442:4(1-27)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3673244
Lee SKo GSong HJung JSerra ESpezzano F(2024)MuLe: Multi-Grained Graph Learning for Multi-Behavior RecommendationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679709(1163-1173)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679709
Bing RYuan GCai ZLi BZhou Y(2024)Automated Modeling of Influence Diversity with Graph Convolutional Network for Social RecommendationWeb and Big Data10.1007/978-981-97-7235-3_3(33-49)Online publication date: 28-Aug-2024
https://doi.org/10.1007/978-981-97-7235-3_3
Kim TChung W(2023)Collaborative Social Metric Learning in Trust Network for Recommender SystemsInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.31653519:1(1-15)Online publication date: 20-Jan-2023
https://dl.acm.org/doi/10.4018/IJSWIS.316535
Lu HDeng XLu J(2023)Research on Efficient Multi-Behavior Recommendation Method Fused with Graph Neural NetworkElectronics10.3390/electronics1209210612:9(2106)Online publication date: 4-May-2023
https://doi.org/10.3390/electronics12092106
Alazaidah RSamara GAlmatarneh SHassan MAljaidi MMansur H(2023)Multi-Label Classification Based on AssociationsApplied Sciences10.3390/app1308508113:8(5081)Online publication date: 19-Apr-2023
https://doi.org/10.3390/app13085081
Wang CZhu HZhu CQin CChen EXiong H(2023)SetRank: A Setwise Bayesian Approach for Collaborative Ranking in Recommender SystemACM Transactions on Information Systems10.1145/362619442:2(1-32)Online publication date: 3-Oct-2023
https://dl.acm.org/doi/10.1145/3626194
Zhang LZhang WWu LHe MZhao H(2023)SHGCN: Socially Enhanced Heterogeneous Graph Convolutional Network for Multi-behavior PredictionACM Transactions on the Web10.1145/361751018:1(1-27)Online publication date: 11-Oct-2023
https://dl.acm.org/doi/10.1145/3617510
Luo XWu DGu YChen CLiu LMa JZhang MDeng MHuang JHua X(2023)Criterion-based Heterogeneous Collaborative Filtering for Multi-behavior Implicit RecommendationACM Transactions on Knowledge Discovery from Data10.1145/361131018:1(1-26)Online publication date: 6-Sep-2023
https://dl.acm.org/doi/10.1145/3611310
Meng CZhai CYang YZhang HLi XFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Parallel Knowledge Enhancement based Framework for Multi-behavior RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615004(1797-1806)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615004
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