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Volume 27 Issue 6
November  2012
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Hui-Feng Sun, Student, Jun-Liang Chen, Gang Yu, Chuan-Chang Liu, Yong Peng, Guang Chen, Bo Cheng. JacUOD: A New Similarity Measurement for Collaborative Filtering[J]. Journal of Computer Science and Technology, 2012, 27(6): 1252-1260. DOI: 10.1007/s11390-012-1301-5
Citation: Hui-Feng Sun, Student, Jun-Liang Chen, Gang Yu, Chuan-Chang Liu, Yong Peng, Guang Chen, Bo Cheng. JacUOD: A New Similarity Measurement for Collaborative Filtering[J]. Journal of Computer Science and Technology, 2012, 27(6): 1252-1260. DOI: 10.1007/s11390-012-1301-5
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JacUOD: A New Similarity Measurement for Collaborative Filtering

  • 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

Funds: 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.
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  • Received Date: August 22, 2011
  • Revised Date: January 18, 2012
  • Published Date: November 04, 2012
    Abstract
  • 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.
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