Abstract-This paper proposes a new similarity measures for User-based collaborative filtering recommender system. The similarity measures for two users are based on the Implication intensity measures. It is called statistical implicative similarity measures (SIS). This similarity measures is applied to build the experimental framework for User-based collaborative filtering recommender model. The experiments on MovieLense dataset show that the model using our similarity measures has fairly accurate results compared with User-based collaborative filtering model using traditional similarity measures as Pearson correlation, Cosine similarity, and Jaccard.
This article proposes a new method to overcome the sparse data problem of the collaborative filtering models (CF models) by considering the homologous relationship between users or items calculated on contextual attributes when we build the CF models. In the traditional CF models, the results are built only based on data from the user's ratings for items. The results of the proposed models are calculated on two factors: (1) the similar factors based on rating values; (2) the similar factors based on contextual attributes. The findings from the experimentation on two datasets DePaulMovie and InCarMusic, show that the proposed models have higher accuracy than the traditional CF models. INDEX TERMS CIBCF models, contextual attributes, context-similarity matrix, CUBCF models.
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