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Bayesian Personalized Ranking with Count Data
횟수 자료를 이용한 베이지안 개인화 순위

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Authors
김동우
Advisor
박병욱
Major
자연과학대학 통계학과
Issue Date
2017-08
Publisher
서울대학교 대학원
Keywords
Recommendation systemImplicit feedbackCount dataMatrix factorizationBayesian personalized ranking (BPR)
Description
학위논문 (석사)-- 서울대학교 대학원 자연과학대학 통계학과, 2017. 8. 박병욱.
Abstract
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.
Language
English
URI
https://hdl.handle.net/10371/138091
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College of Natural Sciences (자연과학대학)Dept. of Statistics (통계학과)Theses (Master's Degree_통계학과)
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