A Collaborative Filtering Recommendation Algorithm Based on Item Probability Distribution
Wang Yong1(),Deng Jiangzhou1,Deng Yongheng1,Zhang Pu2
1Key Laboratory of Electronic Commerce and Logistics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China 2College of Computer Science, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
[Objective] This study tries to reduce the reliance of co-rated items in the traditional item similarity measurements and then improve the prediction precision of the sparse datasets. [Methods] First, we modified the Kullback-Leibler (KL) divergence from the signal processing domain to compute item similarities. Second, we calculated the similarity with the help of density distribution of ratings, and then found the neighboring items more effectively. [Results] We examined the proposed algorithm on MovieLens and the achieved F1 measure value was over 0.65. The accuracy, efficiency and error rates of the new prediction mechanism were much better than traditional item similarity measurements. [Limitations] The proposed algorithm considered the density of ratings, however, it did not utilize the absolute value of item ratings. [Conclusions] The proposed algorithm effectively uses the rating information to address the sparse dataset issue. Thus, it has strong potentiality in practice.
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Wang Yong,Deng Jiangzhou,Deng Yongheng,Zhang Pu. A Collaborative Filtering Recommendation Algorithm Based on Item Probability Distribution. New Technology of Library and Information Service, 2016, 32(6): 73-79.
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