Statistical Implicative Similarity Measures for User-based Collaborative Filtering Recommender System
Nghia Quoc, Phuong Hoai, Hiep Xuan · International Journal of Advanced Computer Science and Applications · 2016
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.