An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
MovieLens unplugged: experiences with an occasionally connected recommender system
Proceedings of the 8th international conference on Intelligent user interfaces
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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The cold-start problem is a primary factor causing performance loss in collaborative filtering. In this paper, we examine a fatal flaw of existing similarity measures in the cold-start condition. We propose a novel method, MSRV, using the moment of a random variable to solve the weaknesses of existing similarity measures that contain vector cosine similarity and correlation analysis-based methods. The proposed method is based on a prudent concept; if the expectation of the difference between two random variables is low, they will be similar to each other. We improve memory-based collaborative filtering performance using the moment that is a major statistical parameter. An experiment using various datasets confirms that the proposed method demonstrates significantly improved prediction performance compared to existing measures in full rating experiments.