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http://dx.doi.org/10.5351/KJAS.2017.30.6.1015

Modified Bayesian personalized ranking for non-binary implicit feedback  

Kim, Dongwoo (Department of Statistics, Seoul National University)
Lee, Eun Ryung (Department of Statistics, Sungkyunkwan University)
Publication Information
The Korean Journal of Applied Statistics / v.30, no.6, 2017 , pp. 1015-1025 More about this Journal
Abstract
Bayesian personalized ranking (BPR) is a state-of-the-art recommendation system techniques for implicit feedback data. Unfortunately, there might be a loss of information because the BPR model considers only the binary transformation of implicit feedback that is non-binary data in most cases. We propose a modified BPR method using a level of confidence based on the size or strength of implicit feedback to overcome this limitation. The proposed method is useful because it still has a structure of interpretable models for underlying personalized ranking i.e., personal pairwise preferences as in the BPR and that it is capable to reflect a numerical size or the strength of implicit feedback. We propose a computation algorithm based on stochastic gradient descent for the numerical implementation of our proposal. Furthermore, we also show the usefulness of our proposed method compared to ordinary BPR via an analysis of steam video games data.
Keywords
recommendation system; implicit feedback; level of confidence; matrix factorization; Bayesian personalized ranking;
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