S-Space College of Natural Sciences (자연과학대학) Dept. of Statistics (통계학과) Theses (Master's Degree_통계학과)
Bayesian Personalized Ranking with Count Data
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- 자연과학대학 통계학과
- Issue Date
- 서울대학교 대학원
- Recommendation system; Implicit feedback; Count data; Matrix factorization; Bayesian personalized ranking (BPR)
- 학위논문 (석사)-- 서울대학교 대학원 자연과학대학 통계학과, 2017. 8. 박병욱.
- Bayesian personalized ranking (BPR) is one of the state-of-the-art models for implicit feedback. Unfortunately, BPR has an limitation that it considers only the binary form of implicit feedback. In this paper, in order to overcome the limitation, we suggest an adapted version of BPR regarding the numeric value of implicit feedback like count data. Furthermore, we implement our model and original BPR in R and compare the results. This model may be useful to reflect implicit feedback more intensively than BPR.