Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
167 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Adversarial Online Collaborative Filtering (2302.05765v3)

Published 11 Feb 2023 in cs.LG

Abstract: We investigate the problem of online collaborative filtering under no-repetition constraints, whereby users need to be served content in an online fashion and a given user cannot be recommended the same content item more than once. We start by designing and analyzing an algorithm that works under biclustering assumptions on the user-item preference matrix, and show that this algorithm exhibits an optimal regret guarantee, while being fully adaptive, in that it is oblivious to any prior knowledge about the sequence of users, the universe of items, as well as the biclustering parameters of the preference matrix. We then propose a more robust version of this algorithm which operates with general matrices. Also this algorithm is parameter free, and we prove regret guarantees that scale with the amount by which the preference matrix deviates from a biclustered structure. To our knowledge, these are the first results on online collaborative filtering that hold at this level of generality and adaptivity under no-repetition constraints. Finally, we complement our theoretical findings with simple experiments on real-world datasets aimed at both validating the theory and empirically comparing to standard baselines. This comparison shows the competitive advantage of our approach over these baselines.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (44)
  1. A channel coding perspective of collaborative filtering. IEEE Transactions on Information Theory, 57(4):2327–2341, 2011.
  2. Regret in online recommendation systems. Advances in Neural Information Processing Systems, 33:21141–21150, 2020.
  3. Using graph partitioning techniques for neighbour selection in user-based collaborative filtering. In Proceedings of the sixth ACM conference on Recommender systems, pages 213–216, 2012.
  4. Statistical analysis of k-nearest neighbor collaborative recommendation. The Annals of Statistics, 38(3):1568–1592, 2010.
  5. Regret bounds and regimes of optimality for user-user and item-item collaborative filtering. IEEE Transactions on Information Theory, 67(6):4197–4222, 2021.
  6. A latent source model for online collaborative filtering. Advances in neural information processing systems, 27, 2014.
  7. Collaborative filtering with low regret. In Proceedings of the 2016 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Science, pages 207–220, 2016.
  8. Regret analysis of stochastic and nonstochastic multi-armed bandit problems. Foundations and Trends® in Machine Learning, 5(1):1–122, 2012.
  9. Clustered bandits. arXiv preprint arXiv:1206.4169, 2012.
  10. Exact matrix completion via convex optimization. Communications of the ACM, 55(6):111–119, 2012.
  11. Onkar Dabeer. Adaptive collaborating filtering: The low noise regime. In 2013 IEEE International Symposium on Information Theory, pages 1197–1201. IEEE, 2013.
  12. Google news personalization: scalable online collaborative filtering. In Proceedings of the 16th international conference on World Wide Web, pages 271–280, 2007.
  13. Online clustering of bandits. In International Conference on Machine Learning, pages 757–765. PMLR, 2014.
  14. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis), 5(4):1–19, 2015.
  15. J. A. Hartigan. Direct Clustering of a Data Matrix. Journal of the American Statistical Association, 67(337):123–129, 1972. ISSN 01621459. 10.2307/2284710. URL http://dx.doi.org/10.2307/2284710.
  16. Near-optimal algorithms for online matrix prediction. In Conference on Learning Theory, pages 38–1. JMLR Workshop and Conference Proceedings, 2012.
  17. Online matrix completion with side information. Advances in Neural Information Processing Systems, 33:20402–20414, 2020.
  18. Latent bandits revisited. In Advances in Neural Information Processing Systems, volume 33, pages 13423–13433. Curran Associates, Inc., 2020.
  19. Koren Yehuda Hu, Yifan and Chris Volinsky. Collaborative filtering for implicit feedback datasets. In IEEE International Conference on Data Mining (ICDM 2008), pages 263–272, 2008.
  20. Low-rank matrix completion using alternating minimization. In Proceedings of the forty-fifth annual ACM symposium on Theory of computing, pages 665–674, 2013.
  21. Categorized bandits. Advances in Neural Information Processing Systems, 32, 2019.
  22. Bilinear bandits with low-rank structure. In Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 3163–3172. PMLR, 2019.
  23. Efficient frameworks for generalized low-rank matrix bandit problems. In Advances in Neural Information Processing Systems. PMLR, 2022.
  24. Bernoulli rank-1111 bandits for click feedback. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17), 2017.
  25. Matrix completion from a few entries. IEEE transactions on information theory, 56(6):2980–2998, 2010.
  26. Matrix factorization techniques for recommender systems. Computer, 42(8):30–37, 2009.
  27. Sparse stochastic bandits. arXiv preprint arXiv:1706.01383, 2017.
  28. Bandit algorithms. Cambridge University Press, 2020.
  29. Collaborative filtering bandits. In Association for Computing Machinery, SIGIR ’16, page 539–548, New York, NY, USA, 2016.
  30. Top-n recommendation with missing implicit feedback. In Proceedings of the 9th ACM Conference on Recommender Systems, pages 309–312, 2015.
  31. Efficient online recommendation via low-rank ensemble sampling. In Proceedings of the 12th ACM Conference on Recommender Systems, RecSys ’18, page 460–464. Association for Computing Machinery, 2018. ISBN 9781450359016.
  32. Low-rank generalized linear bandit problems. In Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, volume 130 of Proceedings of Machine Learning Research, pages 460–468. PMLR, 2021.
  33. Latent bandits. In International Conference on Machine Learning, pages 136–144. PMLR, 2014.
  34. Restricted strong convexity and weighted matrix completion: Optimal bounds with noise. The Journal of Machine Learning Research, 13(1):1665–1697, 2012.
  35. Optimal algorithms for latent bandits with cluster structure. arXiv preprint arXiv:2301.07040, 2023.
  36. Recommender systems. Communications of the ACM, 40(3):56–58, 1997.
  37. Estimation of high-dimensional low-rank matrices. The Annals of Statistics, 39(2):887–930, 2011.
  38. Bin Cao Nathan N. Liu Rajan Lukose Martin Scholz Qiang Yang Rong Pan, Yunhong Zhou. One-class collaborative filtering. In Eighth IEEE International Conference on Data Mining, 2008.
  39. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web, pages 285–295, 2001.
  40. Ranked bandits in metric spaces: Learning diverse rankings over large document collections. J. Mach. Learn. Res., 14(1):399–436, 2013.
  41. Solving bernoulli rank-one bandits with unimodal thompson sampling. In Proceedings of the 31st International Conference on Algorithmic Learning Theory, volume 117 of Proceedings of Machine Learning Research, pages 862–889. PMLR, 2020.
  42. Unifying nearest neighbors collaborative filtering. In Proceedings of the 8th ACM Conference on Recommender systems, pages 177–184, 2014.
  43. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pages 501–508, 2006.
  44. Interactive collaborative filtering. In Proceedings of the 22nd ACM international conference on Information & Knowledge Management, pages 1411–1420, 2013.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets