A collaborative filtering framework based on both local user similarity and global user similarity |
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Authors: | Heng Luo Changyong Niu Ruimin Shen Carsten Ullrich |
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Affiliation: | (1) Department of Computer Science and Technology, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China |
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Abstract: | Collaborative filtering as a classical method of information retrieval has been widely used in helping people to deal with
information overload. In this paper, we introduce the concept of local user similarity and global user similarity, based on
surprisal-based vector similarity and the application of the concept of maximin distance in graph theory. Surprisal-based
vector similarity expresses the relationship between any two users based on the quantities of information (called surprisal) contained in their ratings. Global user similarity defines two users being similar if they can be connected through their
locally similar neighbors. Based on both of Local User Similarity and Global User Similarity, we develop a collaborative filtering
framework called LS&GS. An empirical study using the MovieLens dataset shows that our proposed framework outperforms other
state-of-the-art collaborative filtering algorithms. |
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Keywords: | Collaborative filtering Similarity measure Information theory |
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