文章基本信息

标题：A Generic Strategy for Cold-Start Rating Prediction Problem Using RAPARE
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作者：Vijayalakshmi S ; Ramya J
期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2018
卷号：6
期号：1
页码：99
DOI：10.15680/IJIRCCE.2017.0601012
出版社：S&S Publications
摘要：Recommender system is one of indispensable components in many e-commerce websites. One of themajor challenges that largely remain open is the cold-start problem, which can be viewed as a barrier that keeps thecold-start users/items away from the existing ones. In this paper, we aim to break through this barrier for cold-startusers/items by the assistance of existing ones. In particular, inspired by the classic Elo Rating System, which has beenwidely adopted in chess tournaments, to propose a novel rating comparison strategy (RAPARE) to learn the latentprofiles of cold-start users/items. The centre piece of our RAPARE is to provide a fine-grained calibration on the latentprofiles of cold-start users/items by exploring the differences between cold-start and existing users/items. As a genericstrategy, our proposed strategy can be instantiated into existing methods in recommender systems. To reveal thecapability of RAPARE strategy, we instantiate our strategy on two prevalent methods in recommender systems, i.e., thematrix factorization based and neighbourhood based collaborative filtering
关键词：Recommender systems; cold-start problem; rating comparison strategy