Abstract
Collaborative filtering used in recommender systems produces predictions about the interests of a user by collecting preferences or taste information from many users. A substantial part of collaborative filtering centers on similarity computation. The most popular similarity metrics are Pearson correlation, cosine constrained Pearson’s correlation, Spearman rank correlation and mean squared difference. In this paper, we propose a new metric to compute the similarity between two users, based on Jaccard similarity, inter1-link similarity and inter0-link similarity. Extensive experimental results and comparisons with other existing recommendation methods based on MovieLens dataset show our proposed recommender system is more effective than traditional collaborative filtering algorithms in terms of accuracy.
We acknowledge Jianxin Li for his contributions to the algorithm of this work. We also thank the anonymous reviewers for their valuable comments and help in improving this paper.This work is supported by National Nature Science Foundation of China under Grant No.61202424, and by the National Key Technology R&D Program under Grant No.2012BAH46B04.
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Ma, X., Li, B., An, Q. (2013). A Network-Based Approach for Collaborative Filtering Recommendation. In: Cao, L., et al. Behavior and Social Computing. BSIC BSI 2013 2013. Lecture Notes in Computer Science(), vol 8178. Springer, Cham. https://doi.org/10.1007/978-3-319-04048-6_11
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DOI: https://doi.org/10.1007/978-3-319-04048-6_11
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