research-article

Interactive collaborative filtering

Authors:

Jun WangAuthors Info & Claims

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

Pages 1411 - 1420

https://doi.org/10.1145/2505515.2505690

Published: 27 October 2013 Publication History

Abstract

In this paper, we study collaborative filtering (CF) in an interactive setting, in which a recommender system continuously recommends items to individual users and receives interactive feedback. Whilst users enjoy sequential recommendations, the recommendation predictions are constantly refined using up-to-date feedback on the recommended items. Bringing the interactive mechanism back to the CF process is fundamental because the ultimate goal for a recommender system is about the discovery of interesting items for individual users and yet users' personal preferences and contexts evolve over time during the interactions with the system. This requires us not to distinguish between the stages of collecting information to construct the user profile and making recommendations, but to seamlessly integrate these stages together during the interactive process, with the goal of maximizing the overall recommendation accuracy throughout the interactions. This mechanism naturally addresses the cold-start problem as any user can immediately receive sequential recommendations without providing ratings beforehand. We formulate the interactive CF with the probabilistic matrix factorization (PMF) framework, and leverage several exploitation-exploration algorithms to select items, including the empirical Thompson sampling and upper confidence bound based algorithms. We conduct our experiment on cold-start users as well as warm-start users with drifting taste. Results show that the proposed methods have significant improvements over several strong baselines for the MovieLens, EachMovie and Netflix datasets.

References

[1]

J. Abernethy, K. Amin, M. Draief, and M. Kearns. Large-scale bandit problems and kwik learning.

[2]

H. Ahn. A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Information Sciences, 2008.

Digital Library

[3]

X. Amatriain, J. Pujol, N. Tintarev, and N. Oliver. Rate it again: increasing recommendation accuracy by user re-rating. In RecSys, 2009.

Digital Library

[4]

P. Anick, A. Gourlay, and J. Thrall. Systems and methods for interactive search query refinement, Sept. 20 2005. US Patent 6,947,930.

[5]

P. Auer. Using confidence bounds for exploitation-exploration trade-offs. The Journal of Machine Learning Research, 2003.

Digital Library

[6]

P. Auer, N. Cesa-Bianchi, and P. Fischer. Finite-time analysis of the multiarmed bandit problem. Machine learning, 2002.

Digital Library

[7]

P. Auer, N. Cesa-Bianchi, Y. Freund, and R. Schapire. The nonstochastic multiarmed bandit problem. SIAM Journal on Computing, 2002.

Digital Library

[8]

O. Chapelle and L. Li. An empirical evaluation of thompson sampling. In Neural Information Processing Systems (NIPS), 2011.

[9]

T. Cormen, C. Leiserson, R. Rivest, and C. Stein. Greedy algorithms. Introduction to algorithms, 2001.

[10]

V. Dani, T. Hayes, and S. Kakade. Stochastic linear optimization under bandit feedback. In COLT, 2008.

[11]

A. S. Das, M. Datar, A. Garg, and S. Rajaram. Google news personalization: scalable online collaborative filtering. In WWW, 2007.

Digital Library

[12]

M. Deshpande and G. Karypis. Item-based top-N recommendation algorithms. ACM Trans. Inf. Syst., 2004.

Digital Library

[13]

M. Dudik, D. Hsu, S. Kale, N. Karampatziakis, J. Langford, L. Reyzin, and T. Zhang. Efficient optimal learning for contextual bandits. arXiv preprint arXiv:1106.2369, 2011.

[14]

S. Filippi, O. Cappé, A. Garivier, and C. Szepesvári. Parametric bandits: The generalized linear case. Advances in Neural Information Processing Systems, 2010.

[15]

J. Gittins, K. Glazebrook, and R. Weber. Multi-armed bandit allocation indices. Wiley, 2011.

[16]

N. Golbandi, Y. Koren, and R. Lempel. On bootstrapping recommender systems. In CIKM, 2010.

Digital Library

[17]

N. Golbandi, Y. Koren, and R. Lempel. Adaptive bootstrapping of recommender systems using decision trees. In WSDM, 2011.

Digital Library

[18]

A. Harpale and Y. Yang. Personalized active learning for collaborative filtering. In SIGIR, 2008.

Digital Library

[19]

J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In SIGIR, 1999.

Digital Library

[20]

T. Hofmann and J. Puzicha. Latent class models for collaborative filtering. In Proc. of IJCAI, 1999.

Digital Library

[21]

R. Jin and L. Si. A bayesian approach toward active learning for collaborative filtering. In UAI, 2004.

Digital Library

[22]

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

Digital Library

[23]

P. Lamere and S. Green. Project aura: recommendation for the rest of us. Presentation at Sun JavaOne Conference, 2008.

[24]

J. Langford and T. Zhang. The epoch-greedy algorithm for contextual multi-armed bandits. Advances in Neural Information Processing Systems, 2007.

[25]

L. Li, W. Chu, J. Langford, and R. Schapire. A contextual-bandit approach to personalized news article recommendation. In WWW, 2010.

Digital Library

[26]

L. Li, W. Chu, J. Langford, and X. Wang. Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms. In WSDM, 2011.

Digital Library

[27]

W. Li, X. Wang, R. Zhang, Y. Cui, J. Mao, and R. Jin. Exploitation and exploration in a performance based contextual advertising system. In SIGKDD, 2010.

Digital Library

[28]

N. Liu and Q. Yang. Eigenrank: a ranking-oriented approach to collaborative filtering. In SIGIR, 2008.

Digital Library

[29]

D. Maltz and K. Ehrlich. Pointing the way: active collaborative filtering. In CHI, 1995.

Digital Library

[30]

A. Rashid, I. Albert, D. Cosley, S. Lam, S. McNee, J. Konstan, and J. Riedl. Getting to know you: learning new user preferences in recommender systems. In IUI, 2002.

Digital Library

[31]

A. Rashid, G. Karypis, and J. Riedl. Learning preferences of new users in recommender systems: an information theoretic approach. ACM SIGKDD Explorations Newsletter, 2008.

Digital Library

[32]

N. Rubens, D. Kaplan, and M. Sugiyama. Active learning in recommender systems. Recommender Systems Handbook, pages 735--767, 2011.

[33]

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

[34]

G. Salton and C. Buckley. Improving retrieval performance by relevance feedback. Readings in information retrieval, 1997.

Digital Library

[35]

A. I. Schein, A. Popescul, L. H. Ungar, and D. M. Pennock. Methods and metrics for cold-start recommendations. In Proc. of SIGIR, 2002.

Digital Library

[36]

Y. Shi, X. Zhao, J. Wang, M. Larson, and A. Hanjalic. Adaptive diversification of recommendation results via latent factor portfolio. In SIGIR, 2012.

Digital Library

[37]

T. Walsh, I. Szita, C. Diuk, and M. Littman. Exploring compact reinforcement-learning representations with linear regression. In UAI, 2009.

Digital Library

[38]

J. Wang, A. P. de Vries, and M. J. T. Reinders. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In SIGIR, 2006.

Digital Library

[39]

M. Weimer, A. Karatzoglou, Q. Le, A. Smola, et al. Cofirank-maximum margin matrix factorization for collaborative ranking. In NIPS, 2007.

[40]

K. Zhou, S. Yang, and H. Zha. Functional matrix factorizations for cold-start recommendation. In SIGIR, 2011.

Digital Library

Cited By

Vasconcelos NReis DSilva TSilva NCunha WTuler ERocha L(2024)Integrando Avaliações Textuais de Usuários em Recomendação baseada em Aprendizado por ReforçoProceedings of the 30th Brazilian Symposium on Multimedia and the Web (WebMedia 2024)10.5753/webmedia.2024.241405(390-394)Online publication date: 14-Oct-2024
https://doi.org/10.5753/webmedia.2024.241405
Zhang JXie RHou YZhao XLin LWen J(2024)Recommendation as Instruction Following: A Large Language Model Empowered Recommendation ApproachACM Transactions on Information Systems10.1145/3708882Online publication date: 20-Dec-2024
https://doi.org/10.1145/3708882
Zou JSun ALong CKanoulas E(2024)Knowledge-Enhanced Conversational Recommendation via Transformer-Based Sequential ModelingACM Transactions on Information Systems10.1145/367737642:6(1-27)Online publication date: 18-Oct-2024
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Index Terms

Interactive collaborative filtering
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

cover image ACM Conferences

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

October 2013

2612 pages

ISBN:9781450322638

DOI:10.1145/2505515

General Chairs:
Qi He
LinkedIn, USA
,
Arun Iyengar
IBM T.J. Watson Research Center, USA
,
Program Chairs:
Wolfgang Nejdl
L3S Research Center, Germany
,
Jian Pei
Simon Fraser University, Canada
,
Rajeev Rastogi
Amazon, India

Copyright © 2013 ACM.

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Publication History

Published: 27 October 2013

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CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 27 - November 1, 2013

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CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Vasconcelos NReis DSilva TSilva NCunha WTuler ERocha L(2024)Integrando Avaliações Textuais de Usuários em Recomendação baseada em Aprendizado por ReforçoProceedings of the 30th Brazilian Symposium on Multimedia and the Web (WebMedia 2024)10.5753/webmedia.2024.241405(390-394)Online publication date: 14-Oct-2024
https://doi.org/10.5753/webmedia.2024.241405
Zhang JXie RHou YZhao XLin LWen J(2024)Recommendation as Instruction Following: A Large Language Model Empowered Recommendation ApproachACM Transactions on Information Systems10.1145/3708882Online publication date: 20-Dec-2024
https://doi.org/10.1145/3708882
Zou JSun ALong CKanoulas E(2024)Knowledge-Enhanced Conversational Recommendation via Transformer-Based Sequential ModelingACM Transactions on Information Systems10.1145/367737642:6(1-27)Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1145/3677376
Göpfert CHaig AHsu CChow YVendrov ILu TRamachandran DPham HGhavamzadeh MBoutilier C(2024)Discovering Personalized Semantics for Soft Attributes in Recommender Systems Using Concept Activation VectorsACM Transactions on Recommender Systems10.1145/36586752:4(1-37)Online publication date: 31-Jul-2024
https://dl.acm.org/doi/10.1145/3658675
Austin DKorikov AToroghi ASanner S(2024)Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference ElicitationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688142(74-83)Online publication date: 8-Oct-2024
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Randone FBortolussi LIncerto ETribastone M(2024)Inference of Probabilistic Programs with Moment-Matching Gaussian MixturesProceedings of the ACM on Programming Languages10.1145/36329058:POPL(1882-1912)Online publication date: 5-Jan-2024
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Stray JHalevy AAssar PHadfield-Menell DBoutilier CAshar ABakalar CBeattie LEkstrand MLeibowicz CMoon Sehat CJohansen SKerlin LVickrey DSingh SVrijenhoek SZhang AAndrus MHelberger NProutskova PMitra TVasan N(2024)Building Human Values into Recommender Systems: An Interdisciplinary SynthesisACM Transactions on Recommender Systems10.1145/36322972:3(1-57)Online publication date: 5-Jun-2024
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Kostric IBalog KRadlinski F(2024)Generating Usage-related Questions for Preference Elicitation in Conversational Recommender SystemsACM Transactions on Recommender Systems10.1145/36299812:2(1-24)Online publication date: 10-Apr-2024
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Shen QWu LZhang YPang YWei ZXu FLong BPei J(2024)Multi-Interest Multi-Round Conversational Recommendation System with Fuzzy Feedback Based User SimulatorACM Transactions on Recommender Systems10.1145/36163792:4(1-29)Online publication date: 31-Jul-2024
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Feng KKoo XTan LBruckman AMcDonald DZhang A(2024)Mapping the Design Space of Teachable Social Media Feed ExperiencesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642120(1-20)Online publication date: 11-May-2024
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