More Web Proxy on the site http://driver.im/

research-article

Social collaborative retrieval

Authors:

Alfred HeroAuthors Info & Claims

WSDM '14: Proceedings of the 7th ACM international conference on Web search and data mining

Pages 293 - 302

https://doi.org/10.1145/2556195.2556255

Published: 24 February 2014 Publication History

Abstract

Socially-based recommendation systems have recently attracted significant interest, and a number of studies have shown that social information can dramatically improve a system's predictions of user interests. Meanwhile, there are now many potential applications that involve aspects of both recommendation and information retrieval, and the task of collaborative retrieval---a combination of these two traditional problems---has recently been introduced. Successful collaborative retrieval requires overcoming severe data sparsity, making additional sources of information, such as social graphs, particularly valuable. In this paper we propose a new model for collaborative retrieval, and show that our algorithm outperforms current state-of-the-art approaches by incorporating information from social networks. We also provide empirical analyses of the ways in which cultural interests propagate along a social graph using a real-world music dataset.

References

[1]

Punam Bedi, Harmeet Kaur, and Sudeep Marwaha. Trust based recommender system for semantic web. In proceedings of the 2007 International Joint Conferences on Artificial Intelligence, pages 2677--2682, 2007.

Digital Library

[2]

David M Blei, Andrew Y Ng, and Michael I Jordan. Latent dirichlet allocation. the Journal of machine Learning research, 3: 993--1022, 2003.

Digital Library

[3]

John S Breese, David Heckerman, and Carl Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, pages 43--52. Morgan Kaufmann Publishers Inc., 1998.

Digital Library

[4]

Ivan Cantador, Peter Brusilovsky, and Tsvi Kuflik. Second workshop on information heterogeneity and fusion in recommender systems (hetrec2011). In Proceedings of the fifth ACM conference on Recommender systems, pages 387--388. ACM, 2011.

Digital Library

[5]

David Carmel, Naama Zwerdling, Ido Guy, Shila Ofek-Koifman, Nadav Har'El, Inbal Ronen, Erel Uziel, Sivan Yogev, and Sergey Chernov. Personalized social search based on the user's social network. In Proceedings of the 18th ACM conference on Information and knowledge management, pages 1227--1236. ACM, 2009.

Digital Library

[6]

Alexandros Karatzoglou, Xavier Amatriain, Linas Baltrunas, and Nuria Oliver. Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In Proceedings of the fourth ACM conference on Recommender systems, pages 79--86. ACM, 2010.

Digital Library

[7]

Henry Kautz, Bart Selman, and Mehul Shah. Referral web: combining social networks and collaborative filtering. Communications of the ACM, 40 (3): 63--65, 1997.

Digital Library

[8]

Ioannis Konstas, Vassilios Stathopoulos, and Joemon M Jose. On social networks and collaborative recommendation. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, pages 195--202. ACM, 2009.

Digital Library

[9]

Greg Linden, Brent Smith, and Jeremy York. Amazon. com recommendations: Item-to-item collaborative filtering. Internet Computing, IEEE, 7 (1): 76--80, 2003.

Digital Library

[10]

Hao Ma, Haixuan Yang, Michael R Lyu, and Irwin King. Sorec: social recommendation using probabilistic matrix factorization. In Proceedings of the 17th ACM conference on Information and knowledge management, pages 931--940. ACM, 2008.

Digital Library

[11]

Hao Ma, Irwin King, and Michael 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, pages 203--210. ACM, 2009.

Digital Library

[12]

Hao Ma, Dengyong Zhou, Chao Liu, Michael R Lyu, and Irwin King. Recommender systems with social regularization. In Proceedings of the fourth ACM international conference on Web search and data mining, pages 287--296. ACM, 2011.

Digital Library

[13]

Paolo Massa and Paolo Avesani. Trust-aware collaborative filtering for recommender systems. In On the Move to Meaningful Internet Systems 2004: CoopIS, DOA, and ODBASE, pages 492--508. Springer, 2004.

[14]

Paolo Massa and Paolo Avesani. Trust-aware recommender systems. In Proceedings of the 2007 ACM conference on Recommender systems, pages 17--24. ACM, 2007.

Digital Library

[15]

John O'Donovan and Barry Smyth. Trust in recommender systems. In Proceedings of the 10th international conference on Intelligent user interfaces, pages 167--174. ACM, 2005.

Digital Library

[16]

Sanjay Purushotham, Yan Liu, and C-C Jay Kuo. Collaborative topic regression with social matrix factorization for recommendation systems. arXiv preprint arXiv:1206.4684, 2012.

[17]

Ruslan Salakhutdinov and Andriy Mnih. Probabilistic matrix factorization. Advances in neural information processing systems, 20: 1257--1264, 2008.

[18]

Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web, pages 285--295. ACM, 2001.

Digital Library

[19]

D Seung and L Lee. Algorithms for non-negative matrix factorization. Advances in neural information processing systems, 13: 556--562, 2001.

[20]

Chirag Shah. Lessons and challenges for collaborative information seeking (cis) systems developers. Group, 2009.

[21]

Xiaoyuan Su and Taghi M Khoshgoftaar. A survey of collaborative filtering techniques. Advances in Artificial Intelligence, 2009: 4, 2009.

Digital Library

[22]

Panagiotis Symeonidis, Alexandros Nanopoulos, and Yannis Manolopoulos. Tag recommendations based on tensor dimensionality reduction. In Proceedings of the 2008 ACM conference on Recommender systems, pages 43--50. ACM, 2008.

Digital Library

[23]

Jason Weston, Samy Bengio, and Nicolas Usunier. Large scale image annotation: learning to rank with joint word-image embeddings. Machine learning, 81 (1): 21--35, 2010.

Digital Library

[24]

Jason Weston, Chong Wang, Ron Weiss, and Adam Berenzweig. Latent collaborative retrieval. In Proceedings of the 29th International Conference on Machine Learning (ICML-12), pages 9--16, 2012.

[25]

Liang Xiong, Xi Chen, Tzu-Kuo Huang, Jeff Schneider, and Jaime G Carbonell. Temporal collaborative filtering with bayesian probabilistic tensor factorization. In Proceedings of SIAM Data Mining, volume 2010, 2010.

[26]

Vincent W Zheng, Bin Cao, Yu Zheng, Xing Xie, and Qiang Yang. Collaborative filtering meets mobile recommendation: A user-centered approach. In Proceedings of the 24rd AAAI Conference on Artificial Intelligence, 2010.

Cited By

Wu SLiu QWang LTan TWu SLiu QWang LTan T(2018)Context-Aware Collaborative PredictionContext-Aware Collaborative Prediction10.1007/978-981-10-5373-3_2(7-17)Online publication date: 11-Mar-2018
https://doi.org/10.1007/978-981-10-5373-3_2
Ranganath SWang SHu XTang JLiu H(2017)Facilitating Time Critical Information Seeking in Social MediaIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.270137529:10(2197-2209)Online publication date: 1-Oct-2017
https://doi.org/10.1109/TKDE.2017.2701375
Zhang WWang JBailey JMoffat AAggarwal Cde Rijke MKumar RMurdock VSellis TYu J(2015)Location and Time Aware Social Collaborative Retrieval for New Successive Point-of-Interest RecommendationProceedings of the 24th ACM International on Conference on Information and Knowledge Management10.1145/2806416.2806564(1221-1230)Online publication date: 17-Oct-2015
https://dl.acm.org/doi/10.1145/2806416.2806564
Show More Cited By

Index Terms

Social collaborative retrieval
1. Information systems
  1. Information retrieval

Recommendations

Deep social collaborative filtering by trust
ICBDT '18: Proceedings of the 1st International Conference on Big Data Technologies

The primary purpose of recommender system is providing valuable and pertinent information actively according to users' preference. Collaborative filtering (CF) is a successful approach among different recommendation techniques. However, CF-based methods ...
Aggregating preference graphs for collaborative rating prediction
RecSys '10: Proceedings of the fourth ACM conference on Recommender systems

Collaborative filtering is a widely used technique for rating prediction in recommender systems. Memory based collaborative filtering algorithms assign weights to the users to capture similarities between them. The weighted average of similar users' ...
Incorporating social networks and user opinions for collaborative recommendation: local trust network based method
CAMRa '10: Proceedings of the Workshop on Context-Aware Movie Recommendation

The sparse nature of historical rating profile hinders reliable similarity metrics between users, leading to poor recommendation performance. The availability of user social networks and user opinions can be incorporated to improve prediction accuracy. ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

WSDM '14: Proceedings of the 7th ACM international conference on Web search and data mining

February 2014

712 pages

ISBN:9781450323512

DOI:10.1145/2556195

General Chairs:
Ben Carterette
University of Delaware, USA
,
Fernando Diaz
Microsoft Research, USA
,
Program Chairs:
Carlos Castillo
Qatar Computing Research Institute, Qatar
,
Donald Metzler
Google, USA

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 the author(s) 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: 24 February 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

WSDM 2014

Sponsor:

WSDM 2014: Seventh ACM International Conference on Web Search and Data Mining

February 24 - 28, 2014

New York, New York, USA

Acceptance Rates

WSDM '14 Paper Acceptance Rate 64 of 355 submissions, 18%;

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

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
355
Total Downloads

Downloads (Last 12 months)9
Downloads (Last 6 weeks)5

Reflects downloads up to 11 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Wu SLiu QWang LTan TWu SLiu QWang LTan T(2018)Context-Aware Collaborative PredictionContext-Aware Collaborative Prediction10.1007/978-981-10-5373-3_2(7-17)Online publication date: 11-Mar-2018
https://doi.org/10.1007/978-981-10-5373-3_2
Ranganath SWang SHu XTang JLiu H(2017)Facilitating Time Critical Information Seeking in Social MediaIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.270137529:10(2197-2209)Online publication date: 1-Oct-2017
https://doi.org/10.1109/TKDE.2017.2701375
Zhang WWang JBailey JMoffat AAggarwal Cde Rijke MKumar RMurdock VSellis TYu J(2015)Location and Time Aware Social Collaborative Retrieval for New Successive Point-of-Interest RecommendationProceedings of the 24th ACM International on Conference on Information and Knowledge Management10.1145/2806416.2806564(1221-1230)Online publication date: 17-Oct-2015
https://dl.acm.org/doi/10.1145/2806416.2806564
Liu QWu SWang LBailey JMoffat AAggarwal Cde Rijke MKumar RMurdock VSellis TYu J(2015)Collaborative Prediction for Multi-entity Interaction With Hierarchical RepresentationProceedings of the 24th ACM International on Conference on Information and Knowledge Management10.1145/2806416.2806530(613-622)Online publication date: 17-Oct-2015
https://dl.acm.org/doi/10.1145/2806416.2806530
Yin QWu SWang L(2015)Learning to Hash for Recommendation with Tensor DataWeb Technologies and Applications10.1007/978-3-319-25255-1_24(292-303)Online publication date: 13-Nov-2015
https://doi.org/10.1007/978-3-319-25255-1_24

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