Hyeong-Joon Kwon, Kwang-Seok Hong, Journal of Internet Computing and Services, Vol. 14, No. 5, pp. 59-68, Oct. 2013
10.7472/jksii.2013.14.5.59, Full Text:  HTML
Keywords: Collaborative Filtering, Attribute Preference, Recommender System

Abstract

In this paper, we propose the LAR_CF, latent attribute rating-based collaborative filtering, that is robust to data sparsity problem which is one of traditional problems caused of decreasing rating prediction accuracy. As compared with that existing collaborative filtering method uses a preference rating rated by users as feature vector to calculate similarity between objects, the proposed method improves data sparsity problem using unique attributes of two target objects with existing explicit preference. We consider MovieLens 100k dataset and its item attributes to evaluate the LAR_CF. As a result of artificial data sparsity and full-rating experiments, we confirmed that rating prediction accuracy can be improved rating prediction accuracy in data sparsity condition by the LAR_CF.

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