Collaborative filtering with diffusion-based similarity on tripartite graphs

Details Collaborative filtering with diffusion-based similarity on tripartite graphs Collaborative filtering with diffusion-based similarity on tripartite graphs

2009-06-26

arxiv.org/abs/0906.5017v2

Physica A 389 (2010) 1259-1264

Computer Science

Information Retrieval

8 pages, 4 figures, 1 table

Scientific paper

10.1016/j.physa.2009.11.041

Collaborative tags are playing more and more important role for the organization of information systems. In this paper, we study a personalized recommendation model making use of the ternary relations among users, objects and tags. We propose a measure of user similarity based on his preference and tagging information. Two kinds of similarities between users are calculated by using a diffusion-based process, which are then integrated for recommendation. We test the proposed method in a standard collaborative filtering framework with three metrics: ranking score, Recall and Precision, and demonstrate that it performs better than the commonly used cosine similarity.

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