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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 7 Issue 11, 2016.
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.
Nghia Quoc Phan, Phuong Hoai Dang and Hiep Xuan Huynh, “Statistical Implicative Similarity Measures for User-based Collaborative Filtering Recommender System” International Journal of Advanced Computer Science and Applications(IJACSA), 7(11), 2016. http://dx.doi.org/10.14569/IJACSA.2016.071118
@article{Phan2016,
title = {Statistical Implicative Similarity Measures for User-based Collaborative Filtering Recommender System},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.071118},
url = {http://dx.doi.org/10.14569/IJACSA.2016.071118},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {11},
author = {Nghia Quoc Phan and Phuong Hoai Dang and Hiep Xuan Huynh}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.