research-article

Probabilistic relevance ranking for collaborative filtering

Authors:

Stephen Robertson,

Arjen P. de Vries,

Marcel J. T. ReindersAuthors Info & Claims

Information Retrieval, Volume 11, Issue 6

Pages 477 - 497

https://doi.org/10.1007/s10791-008-9060-1

Published: 01 December 2008 Publication History

Abstract

Collaborative filtering is concerned with making recommendations about items to users. Most formulations of the problem are specifically designed for predicting user ratings, assuming past data of explicit user ratings is available. However, in practice we may only have implicit evidence of user preference; and furthermore, a better view of the task is of generating a top-N list of items that the user is most likely to like. In this regard, we argue that collaborative filtering can be directly cast as a relevance ranking problem. We begin with the classic Probability Ranking Principle of information retrieval, proposing a probabilistic item ranking framework. In the framework, we derive two different ranking models, showing that despite their common origin, different factorizations reflect two distinctive ways to approach item ranking. For the model estimations, we limit our discussions to implicit user preference data, and adopt an approximation method introduced in the classic text retrieval model (i.e. the Okapi BM25 formula) to effectively decouple frequency counts and presence/absence counts in the preference data. Furthermore, we extend the basic formula by proposing the Bayesian inference to estimate the probability of relevance (and non-relevance), which largely alleviates the data sparsity problem. Apart from a theoretical contribution, our experiments on real data sets demonstrate that the proposed methods perform significantly better than other strong baselines.

References

[1]

Adomavicius G. and Tuzhilin A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions IEEE Transactions on Knowledge and Data Engineering 2005 17 6 734-749

[2]

Baeza-Yates, R., & Ribeiro-Neto, B. (1999). Modern information retrieval. Addison Wesley.

[3]

Belkin N. J. and Croft W. B. Information filtering and information retrieval: Two sides of the same coin? Communications of The ACM 1992 35 12 29-38

[4]

Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.

[5]

Breese, J., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Annual Conference on Uncertainty in Artificial Intelligence (UAI-98) (pp. 43–52). San Francisco, CA: Morgan Kaufmann.

[6]

Canny, J. (2002). Collaborative filtering with privacy via factor analysis. In SIGIR ’02: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 238–245). New York, NY: ACM Press.

[7]

Claypool, M., Le, P., Wased, M., & Brown, D. (2001). Implicit interest indicators. In IUI ’01: Proceedings of the 6th International Conference on Intelligent User Interfaces (pp. 33–40). New York, NY, USA: ACM.

[8]

Cooper W. S. Some inconsistencies and misidentified modeling assumptions in probabilistic information retrieval ACM Transactions on Information Systems 1995 13 1 100-111

[9]

Dempster A. P., Laird N. M., and Rubin D. B. Maximum likelihood from incomplete data via the em algorithm Journal of the Royal Statistical Society 1977 39 1 1-38

[10]

Deshpande M. and Karypis G. Item-based top-N recommendation algorithms ACM Transactions on Information Systems 2004 22 1 143-177

[11]

Eyheramendy, S., Lewis, D., & Madigan, D. (2003). On the naive bayes model for text categorization. In Proceeding of the Artificial Intelligence and Statistics.

[12]

Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003). Bayesian data analysis. Chapman and Hall.

[13]

Harter S. A probabilistic approach to automatic keyword indexing Journal of the American Society for Information Science 1975 35 197-206

[14]

Herlocker, J. L., Konstan, J. A., Borchers, A., & Riedl, J. (1999). An algorithmic framework for performing collaborative filtering. In SIGIR '99: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 230–237). New York, NY: ACM Press.

[15]

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

[16]

Hull, D. (1993). Using statistical testing in the evaluation of retrieval experiments. In SIGIR ’93: Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 329–338). New York, NY: ACM Press.

[17]

Jin R., Si L., and Zhai C. A study of mixture models for collaborative filtering Information Retrieval 2006 9 3 357-382

[18]

Jordan, M. (1999). Learning in graphical models. MIT Press.

[19]

Lafferty J. and Zhai C. Probabilistic relevance models based on document and query generation Language Modeling and Information Retrieval, Kluwer International Series on Information Retrieval 2003 V13 1-10

[20]

Marlin, B. (2004). Collaborative filtering: A machine learning perspective. Master’s thesis, Department of Computer Science, University of Toronto.

[21]

McLaughlin, M. R., & Herlocker, J. L. (2004). A collaborative filtering algorithm and evaluation metric that accurately model the user experience. In SIGIR ’04: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 329–336). New York, NY, USA: ACM Press.

[22]

Pennock, D. M., Horvitz, E., Lawrence, S., & Giles, C. L. (2000). Collaborative filtering by personality diagnosis: A hybrid memory and model-based approach. In UAI ’00: Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence (pp. 473–480). San Francisco, CA: Morgan Kaufmann Publishers Inc.

[23]

Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). Grouplens: An open architecture for collaborative filtering of netnews. In CSCW ’94: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work (pp. 175–186). New York, NY: ACM Press.

[24]

Robertson, S. E. (1997). The probability ranking principle in IR. In Readings in information retrieval (pp. 281–286).

[25]

Robertson S. E. and Sparck Jones K. Relevance weighting of search terms Journal of the American Society for Information Science 1976 27 3 129-46

[26]

Robertson S. E., Maron M. E., and Cooper W. Probability of relevance: A unification of two competing models for document retrieval Information Technology: Research and Development 1982 1 1 1-21

[27]

Robertson, S. E., & Walker, S. (1994). Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. In SIGIR’94: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 232–241) New York, NY: Springer-Verlag New York, Inc.

[28]

Sarwar, B., Karypis, G., Konstan, J., & Reidl, J. (2001). Item-based collaborative filtering recommendation algorithms. In WWW ’01: Proceedings of the 10th International Conference on World Wide Web (pp. 285–295) New York, NY: ACM Press.

[29]

Sparck Jones K., Walker S., and Robertson S. E. A probabilistic model of information retrieval: Development and comparative experiments, part1 Information Processing and Management 2000 V36 6 779-808

[30]

Sparck Jones K., Walker S., and Robertson S. E. A probabilistic model of information retrieval: Development and comparative experiments, part 2 Information Processing and Management 2000 36 6 809-840

[31]

Taylor, M. J., Zaragoza, H., & Robertson, S. E. (2003). Ranking classes: Finding similar authors. Technical Report, Microsoft Research, Cambridge.

[32]

van Rijsbergen, C. J. (1979). Information Retrieval. London, UK: Butterworths.

[33]

Wang, J., de Vries, A. P., & Reinders, M. J. T. (2006). A user-item relevance model for log-based collaborative filtering. In Proceedings of the ECIR06, London, UK (pp. 37–48). Berlin, Germany: Springer Berlin/Heidelberg.

[34]

Wang, J., de Vries, A. P., & Reinders, M. J. T. (2008). Unified relevance models for rating prediction in collaborative filtering. ACM Transactions on Information System (TOIS) (to appear).

[35]

Wang, J., de Vries, A. P., & Reinders, M. J. T. (2006). Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In SIGIR ’06: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 501–508). New York, NY: ACM Press.

[36]

Wang, J., Yang, J., Clements, M., de Vries, A. P., & Reinders, M. J. T. (2007). Personalized collaborative tagging. Technical Report, University College London.

[37]

Xue, G.-R., Lin, C., Yang, Q., Xi, W., Zeng, H.-J., Yu, Y., & Chen, Z. (2005). Scalable collaborative filtering using cluster-based smoothing. In SIGIR’ 05: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 114–121). New York, NY: ACM Press.

[38]

Zaragoza, H., Hiemstra, D., & Tipping, M. (2003). Bayesian extension to the language model for ad hoc information retrieval. In SIGIR ’03: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval (pp. 4–9) New York, NY, USA: ACM Press.

[39]

Zhai, C., & Lafferty, J. (2001). A study of smoothing methods for language models applied to ad hoc information retrieval. In SIGIR ’01: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 334–342) New York, NY: ACM Press.

Cited By

Guo XWang LZhang MChen G(2023)First Things First? Order Effects in Online Product Recommender SystemsACM Transactions on Computer-Human Interaction10.1145/355788630:1(1-35)Online publication date: 18-Mar-2023
https://dl.acm.org/doi/10.1145/3557886
Ferrari Dacrema MBoglio SCremonesi PJannach D(2021)A Troubling Analysis of Reproducibility and Progress in Recommender Systems ResearchACM Transactions on Information Systems10.1145/343418539:2(1-49)Online publication date: 6-Jan-2021
https://dl.acm.org/doi/10.1145/3434185
Assadi SBalkanski ELeme RWallach HLarochelle HBeygelzimer Ad'Alché-Buc FFox E(2019)Secretary ranking with minimal inversionsProceedings of the 33rd International Conference on Neural Information Processing Systems10.5555/3454287.3454382(1051-1063)Online publication date: 8-Dec-2019
https://dl.acm.org/doi/10.5555/3454287.3454382
Show More Cited By

Index Terms

Probabilistic relevance ranking for collaborative filtering

Index terms have been assigned to the content through auto-classification.

Recommendations

Ranking-Oriented Collaborative Filtering: A Listwise Approach

Collaborative filtering (CF) is one of the most effective techniques in recommender systems, which can be either rating oriented or ranking oriented. Ranking-oriented CF algorithms demonstrated significant performance gains in terms of ranking accuracy, ...
Integrating collaborative filtering and matching-based search for product recommendations

Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and, thus, help in making good ...
Trust-based collaborative filtering: tackling the cold start problem using regular equivalence
RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems

User-based Collaborative Filtering (CF) is one of the most popular approaches to create recommender systems. This approach is based on finding the most relevant k users from whose rating history we can extract items to recommend. CF, however, suffers ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Information Retrieval

Information Retrieval Volume 11, Issue 6

Dec 2008

84 pages

ISSN:1386-4564

Issue’s Table of Contents

© Springer Science+Business Media, LLC 2008.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 December 2008

Accepted: 09 May 2008

Received: 24 August 2007

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

31
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 16 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Guo XWang LZhang MChen G(2023)First Things First? Order Effects in Online Product Recommender SystemsACM Transactions on Computer-Human Interaction10.1145/355788630:1(1-35)Online publication date: 18-Mar-2023
https://dl.acm.org/doi/10.1145/3557886
Ferrari Dacrema MBoglio SCremonesi PJannach D(2021)A Troubling Analysis of Reproducibility and Progress in Recommender Systems ResearchACM Transactions on Information Systems10.1145/343418539:2(1-49)Online publication date: 6-Jan-2021
https://dl.acm.org/doi/10.1145/3434185
Assadi SBalkanski ELeme RWallach HLarochelle HBeygelzimer Ad'Alché-Buc FFox E(2019)Secretary ranking with minimal inversionsProceedings of the 33rd International Conference on Neural Information Processing Systems10.5555/3454287.3454382(1051-1063)Online publication date: 8-Dec-2019
https://dl.acm.org/doi/10.5555/3454287.3454382
Ferrari Dacrema MCremonesi PJannach DBogers TSaid ABrusilovsky PTikk D(2019)Are we really making much progress? A worrying analysis of recent neural recommendation approachesProceedings of the 13th ACM Conference on Recommender Systems10.1145/3298689.3347058(101-109)Online publication date: 10-Sep-2019
https://dl.acm.org/doi/10.1145/3298689.3347058
Suárez-García ELandin AValcarce DBarreiro ÁTramullas JTrillo-Lado RNogueras-Iso J(2018)Term Association Measures for Memory-based Recommender SystemsProceedings of the 5th Spanish Conference on Information Retrieval10.1145/3230599.3230606(1-8)Online publication date: 26-Jun-2018
https://dl.acm.org/doi/10.1145/3230599.3230606
Huang JWang JYao YZhong N(2017)Cost-sensitive three-way recommendations by learning pair-wise preferencesInternational Journal of Approximate Reasoning10.1016/j.ijar.2017.03.00586:C(28-40)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1016/j.ijar.2017.03.005
Petroni FQuerzoni LBeraldi RPaolucci M(2016)LCBMJournal of Systems and Software10.1016/j.jss.2016.04.062117:C(583-594)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1016/j.jss.2016.04.062
Zhang MGuo XChen G(2016)Prediction uncertainty in collaborative filteringDecision Support Systems10.1016/j.dss.2015.12.00483:C(10-21)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1016/j.dss.2015.12.004
Huang JZhu KZhong N(2016)A probabilistic inference model for recommender systemsApplied Intelligence10.1007/s10489-016-0783-145:3(686-694)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1007/s10489-016-0783-1
Zhao XWang JAllan JCroft Bde Vries AZhai C(2015)A Theoretical Analysis of Two-Stage Recommendation for Cold-Start Collaborative FilteringProceedings of the 2015 International Conference on The Theory of Information Retrieval10.1145/2808194.2809459(71-80)Online publication date: 27-Sep-2015
https://dl.acm.org/doi/10.1145/2808194.2809459
Show More Cited By

View Options

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

Media

Figures

Other

Tables

View Issue’s Table of Contents