›› 2012, Vol. 27 ›› Issue (6): 1252-1260.doi: 10.1007/s11390-012-1301-5

• Artificial Intelligence and Pattern Recognition • Previous Articles     Next Articles

JacUOD: A New Similarity Measurement for Collaborative Filtering

Hui-Feng Sun1 (孙慧峰), Student Member, CCF, ACM, Jun-Liang Chen1 (陈俊亮), Gang Yu1 (俞钢), Member, IEEE, Chuan-Chang Liu1 (刘传昌), Member, IEEE, Yong Peng1 (彭泳), Guang Chen2 (陈光), and Bo Cheng1 (程渤), Senior Member, CCF, Member, ACM   

  1. 1. State Key Lab of Network and Switching Technology, Beijing University of Posts and Telecommunications Beijing 100876, China;
    2. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications Beijing 100876, China
  • Received:2011-08-23 Revised:2012-01-19 Online:2012-11-05 Published:2012-11-05
  • Supported by:

    This work was supported by the National Basic Research 973 Program of China under Grant No. 2011CB302506, the National Natural Science Foundation of China under Grant Nos. 61001118, 61132001, 61003067, the National Major Science and Technology Project of New Generation Broadband Wireless Network of China under Grant No. 2010ZX03004-001, and the Fundamental Research Funds for the Central Universities of Beijing University of Posts and Telecommunications of China under Grant No. 2011RC0502.

Collaborative filtering (CF) has been widely applied to recommender systems, since it can assist users to discover their favorite items. Similarity measurement that measures the similarity between two users or items is critical to CF. However, traditional similarity measurement approaches for memory-based CF can be strongly improved. In this paper, we propose a novel similarity measurement, named Jaccard Uniform Operator Distance (JacUOD), to effectively measure the similarity. Our JacUOD approach aims at unifying similarity comparison for vectors in different multidimensional vector spaces. Compared with traditional similarity measurement approaches, JacUOD properly handles dimension-number difference for different vector spaces. We conduct experiments based on the well-known MovieLens datasets, and take user-based CF as an example to show the effectiveness of our approach. The experimental results show that our JacUOD approach achieves better prediction accuracy than traditional similarity measurement approaches.

CLC Number: 

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