BPR: Bayesian Personalized Ranking from Implicit Feedback

BPR: Bayesian Personalized Ranking from Implicit Feedback

2009 | Steffen Rendle, Christoph Freudenthaler, Zeno Gantner and Lars Schmidt-Thieme
This paper addresses the task of item recommendation, focusing on predicting personalized rankings from implicit feedback such as clicks and purchases. The authors propose a generic optimization criterion called BPR-Opt, derived from a Bayesian analysis, and a learning algorithm called LEARNBPR based on stochastic gradient descent with bootstrap sampling. They demonstrate how to apply these methods to two state-of-the-art recommender models: matrix factorization (MF) and adaptive k-nearest-neighbor (kNN). Experiments show that BPR-Opt outperforms standard learning techniques for MF and kNN in personalized ranking tasks, highlighting the importance of optimizing models for the right criterion. The paper also discusses the relationship between BPR-Opt and other ranking metrics like AUC, and provides a detailed evaluation using two datasets: Rossmann and Netflix. The results confirm that personalized ranking methods, when optimized with BPR-Opt, significantly outperform non-personalized methods.This paper addresses the task of item recommendation, focusing on predicting personalized rankings from implicit feedback such as clicks and purchases. The authors propose a generic optimization criterion called BPR-Opt, derived from a Bayesian analysis, and a learning algorithm called LEARNBPR based on stochastic gradient descent with bootstrap sampling. They demonstrate how to apply these methods to two state-of-the-art recommender models: matrix factorization (MF) and adaptive k-nearest-neighbor (kNN). Experiments show that BPR-Opt outperforms standard learning techniques for MF and kNN in personalized ranking tasks, highlighting the importance of optimizing models for the right criterion. The paper also discusses the relationship between BPR-Opt and other ranking metrics like AUC, and provides a detailed evaluation using two datasets: Rossmann and Netflix. The results confirm that personalized ranking methods, when optimized with BPR-Opt, significantly outperform non-personalized methods.
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Understanding BPR%3A Bayesian Personalized Ranking from Implicit Feedback