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BPR: Bayesian personalized ranking from implicit feedback

Authors:

Steffen Rendle,

Christoph Freudenthaler,

Lars Schmidt-ThiemeAuthors Info & Claims

UAI '09: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence

Pages 452 - 461

Published: 18 June 2009 Publication History

Abstract

Item recommendation is the task of predicting a personalized ranking on a set of items (e.g. websites, movies, products). In this paper, we investigate the most common scenario with implicit feedback (e.g. clicks, purchases). There are many methods for item recommendation from implicit feedback like matrix factorization (MF) or adaptive k-nearest-neighbor (kNN). Even though these methods are designed for the item prediction task of personalized ranking, none of them is directly optimized for ranking. In this paper we present a generic optimization criterion BPR-Opt for personalized ranking that is the maximum posterior estimator derived from a Bayesian analysis of the problem. We also provide a generic learning algorithm for optimizing models with respect to BPR-Opt. The learning method is based on stochastic gradient descent with bootstrap sampling. We show how to apply our method to two state-of-the-art recommender models: matrix factorization and adaptive kNN. Our experiments indicate that for the task of personalized ranking our optimization method outperforms the standard learning techniques for MF and kNN. The results show the importance of optimizing models for the right criterion.

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Cited By

Liu ZWang CZheng SWu CZheng KSong YMou N(2025)Pone-GNN: Integrating Positive and Negative Feedback in Graph Neural Networks for Recommender SystemsACM Transactions on Recommender Systems10.1145/3711666Online publication date: 4-Jan-2025
https://dl.acm.org/doi/10.1145/3711666
Song SLiu BTeng FLi T(2025)Self-supervised contrastive learning for implicit collaborative filteringEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109563139:PAOnline publication date: 1-Jan-2025
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Wu SDu LYang JWang YZhan DZhao SSun ZKiyavash NMooij J(2024)RE-SORTProceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence10.5555/3702676.3702854(3816-3828)Online publication date: 15-Jul-2024
https://dl.acm.org/doi/10.5555/3702676.3702854
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Index Terms

BPR: Bayesian personalized ranking from implicit feedback

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Information & Contributors

Information

Published In

cover image Guide Proceedings

UAI '09: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence

June 2009

667 pages

ISBN:9780974903958

Conference Chair:
David McAllester
Toyota Technological Institute at Chicago

Sponsors

Google Inc.
IBMR: IBM Research
Intel: Intel
Microsoft Research: Microsoft Research

Publisher

AUAI Press

Arlington, Virginia, United States

Publication History

Published: 18 June 2009

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Cited By

Liu ZWang CZheng SWu CZheng KSong YMou N(2025)Pone-GNN: Integrating Positive and Negative Feedback in Graph Neural Networks for Recommender SystemsACM Transactions on Recommender Systems10.1145/3711666Online publication date: 4-Jan-2025
https://dl.acm.org/doi/10.1145/3711666
Song SLiu BTeng FLi T(2025)Self-supervised contrastive learning for implicit collaborative filteringEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109563139:PAOnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.engappai.2024.109563
Wu SDu LYang JWang YZhan DZhao SSun ZKiyavash NMooij J(2024)RE-SORTProceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence10.5555/3702676.3702854(3816-3828)Online publication date: 15-Jul-2024
https://dl.acm.org/doi/10.5555/3702676.3702854
Qi LLiu YLiu WPei SXu XZhang XWang YDou WLarson K(2024)Counterfactual user sequence synthesis augmented with continuous time dynamic preference modeling for sequential POI recommendationProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/255(2306-2314)Online publication date: 3-Aug-2024
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Chen CZhang YLi YWang JQi LXu XZheng XYin J(2024)Post-Training Attribute Unlearning in Recommender SystemsACM Transactions on Information Systems10.1145/370198743:1(1-28)Online publication date: 6-Nov-2024
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Zhang YHu ZBai YWu JWang QFeng F(2024)Recommendation Unlearning via Influence FunctionACM Transactions on Recommender Systems10.1145/37017633:2(1-23)Online publication date: 29-Oct-2024
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Zhang YZhang YSang LSheng V(2024)Simplify to the Limit! Embedding-Less Graph Collaborative Filtering for Recommender SystemsACM Transactions on Information Systems10.1145/370123043:1(1-30)Online publication date: 19-Oct-2024
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