A Combinative Similarity Computing Measure for Collaborative Filtering

Abstract:

Similarity method is the key of the user-based collaborative filtering recommend algorithm. The traditional similarity measures, which cosine similarity, adjusted cosine similarity and Pearson correlation similarity are included, have some advantages such as simple, easy and fast, but with the sparse dataset they may lead to bad recommendation quality. In this article, we first research how the recommendation qualities using the three similarity methods respectively change with the different sparse datasets, and then propose a combinative similarity measure considering the account of items users co-rated. Compared with the three algorithms, our method shows its satisfactory performance with the same computation complexity.

• Periodical: Applied Mechanics and Materials (Volumes 347-350)
• Pages: 2919-2925
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.347-350.2919
• Online since: August 2013
• Authors: Lin Guo, Qin Ke Peng
• Keywords: Items Account Users Co-Rated, Similarity Method, User-Based Collaborative Filtering

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