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http://dx.doi.org/10.3745/KIPSTD.2009.16-D.3.451

Building Error-Reflected Models for Collaborative Filtering Recommender System  

Kim, Heung-Nam (인하대학교 BK21 정보기술사업단)
Jo, Geun-Sik (인하대학교 컴퓨터정보공학과)
Publication Information
The KIPS Transactions:PartD / v.16D, no.3, 2009 , pp. 451-462 More about this Journal
Abstract
Collaborative Filtering (CF), one of the most successful technologies among recommender systems, is a system assisting users in easily finding the useful information. However, despite its success and popularity, CF encounters a serious limitation with quality evaluation, called cold start problems. To alleviate this limitation, in this paper, we propose a unique method of building models derived from explicit ratings and applying the models to CF recommender systems. The proposed method is divided into two phases, an offline phase and an online phase. First, the offline phase is a building pre-computed model phase in which most of tasks can be conducted. Second, the online phase is either a prediction or recommendation phase in which the models are used. In a model building phase, we first determine a priori predicted rating and subsequently identify prediction errors for each user. From this error information, an error-reflected model is constructed. The error-reflected model, which is reflected average prior prediction errors of user neighbors and item neighbors, can make accurate predictions in the situation where users or items have few opinions; this is known as the cold start problems. In addition, in order to reduce the re-building tasks, the error-reflected model is designed such that the model is updated effectively and users'new opinions are reflected incrementally, even when users present a new rating feedback.
Keywords
Recommender System; Collaborative Filtering; Cold Start Problems;
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