ABSTRACT
Bayesian Personalized Ranking (BPR) is a representative pairwise learning method for optimizing recommendation models. It is widely known that the performance of BPR depends largely on the quality of the negative sampler. In this short paper, we make two contributions with respect to BPR. First, we find that sampling negative items from the whole space is unnecessary and may even degrade the performance. Second, focusing on the purchase feedback of the E-commerce domain, we propose a simple yet effective sampler for BPR by leveraging the additional view data. Compared to the vanilla BPR that applies a uniform sampler on all candidates, our view-aware sampler enhances BPR with a relative improvement of 27.36% and 69.54% on two real-world datasets respectively.
- I. Bayer, X. He, B. Kanagal, and S. Rendle. A generic coordinate descent framework for learning from implicit feedback. In WWW, pages 1341--1350, 2017. Google ScholarDigital Library
- X. He and T.-S. Chua. Neural factorization machines for sparse predictive analytics. In SIGIR, pages 355--364, 2017. Google ScholarDigital Library
- X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua. Neural collaborative filtering. In WWW, pages 173--182, 2017. Google ScholarDigital Library
- S. Rendle and C. Freudenthaler. Improving pairwise learning for item recommendation from implicit feedback. In WSDM, pages 273--282, 2014. Google ScholarDigital Library
- S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In UAI, pages 452--461, 2009. Google ScholarDigital Library
- W. Zhang, T. Chen, J. Wang, and Y. Yu. Optimizing top-n collaborative filtering via dynamic negative item sampling. In SIGIR, pages 785--788, 2013. Google ScholarDigital Library
Index Terms
- An Improved Sampler for Bayesian Personalized Ranking by Leveraging View Data
Recommendations
Sampler Design for Bayesian Personalized Ranking by Leveraging View Data
Bayesian Personalized Ranking (BPR) is a representative pairwise learning method for optimizing recommendation models. It is widely known that the performance of BPR depends largely on the quality of negative sampler. In this paper, we make two ...
An enterprise model and the organisation of ERP
Many BPR practitioners have indicated that the application of information technology is critical to the success of their BPR. ERP is currently one of the most popular information systems being employed to help organisations gain competitive advantage. ...
Multi-view visual Bayesian personalized ranking for restaurant recommendation
AbstractIn recent recommendation systems, the image information of items is often used in conjunction with deep convolution network to directly learn the visual features of items. However, the existing approaches usually use only one image to represent an ...
Comments