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New Technology of Library and Information Service  2016, Vol. 32 Issue (6): 73-79    DOI: 10.11925/infotech.1003-3513.2016.06.09
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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
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Abstract  

[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.

Key wordsItem similarity      Collaborative filtering      Kullback-Leibler divergence      Recommendation algorithm     
Received: 26 January 2016      Published: 18 July 2016

Cite this article:

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.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2016.06.09     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2016/V32/I6/73

[1] Goldberg D, Nichols D, Oki B M, et al.Using Collaborative Filtering to Weave an Information Tapestry[J]. Communications of the ACM, 1992, 35(12): 61-70.
[2] Zheng N, Li Q, Liao S, et al.Which Photo Groups Should I Choose a Comparative Study of Recommendation Algorithms in Flickr[J]. Journal of Information Science, 2010, 36(6): 733-750.
[3] Brynjolfsson E, Hu Y J, Smith M D.Consumer Surplus in the Digital Economy: Estimating the Value of Increased Product Variety at Online Booksellers[J]. Management Science, 2003, 49(11): 1580-1596.
[4] Breese J, Hecherman D, Kadie C.Empirical Analysis of Predictive Algorithms for Collaborative Filtering [C]. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, 1998.
[5] Xu C, Xu J, Du X.Recommendation Algorithm Combining the User-based Classified Regression and the Item-based Filtering [C]. In: Proceedings of the International Conference on Electronic Commerce: The New E-commerce-Innovations for Conquering Current Barriers, Obstacles and Limitations to Conducting Successful Business on the Internet, Fredericton, New Brunswick, Canada. 2006: 574-578.
[6] Arwar B, Karypls G, Konstan J, et al.Item-based Collaborative Filtering Recommendation Algorithms [C]. In: Proceedings of the 10th International World Wide Web Conference. 2001.
[7] Kim B M, Li Q, Park C S, et al.A New Approach for Combining Content-based and Collaborative Filters[J]. Journal of Intelligent Information System, 2006, 27(1): 79-91.
[8] Karypis G.Evaluation of Item-based Top-N Recommendation Algorithms[C]. In: Proceedings of the 10th International Conference on Information and Knowledge Management. 2001.
[9] Deng A, Zhu Y, Shi B.A Collaborative Filtering Recommendation Algorithm Based on Item Rating Prediction[J]. Journal of Software, 2003, 14(9): 1621-1628.
[10] Luo H, Niu C, Shen R, et al.A Collaborative Filtering Framework Based on both Local User Similarity and Global User Similarity[J]. Machine Learning, 2008,72(3): 231-245.
[11] Ahn H J.A New Similarity Measure for Collaborative Filtering to Alleviate the New User Cold-Starting Problem[J]. Information Sciences, 2008, 178(1): 37-51.
[12] Bobadilla J, Ortega F, Hernando A, et al.A Collaborative Filtering Approach to Mitigate the New User Cold Start Problem[J]. Knowledge-Based Systems, 2012, 26: 225-238.
[13] Koutrica G, Bercovitz B, Garcia H.FlexRecs: Expressing and Combining Flexible Recommendations [C]. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. 2009.
[14] Cacheda F, Carneiro V, Fernández D, et al.Comparison of Collaborative Filtering Algorithms: Limitations of Current Techniques and Proposals for Scalable, High-Performance Recommender System[J]. ACM Transactions on the Web, 2011, 5(1): 1-33.
[15] Patra B K, Launonen R, Ollikainen V, et al.Exploiting Bhattacharyya Similarity Measure to Diminish User Cold- start Problem in Sparse Data [A]. // Discovery Science [M]. Springer International Publishing, 2014: 252-263.
[16] Kullback S, Leibler R A.On Information and Sufficiency[J]. The Annals of Mathematical Statistics, 1951, 22(1): 79-86.
[17] Huang A.Similarity Measures for Text Document Clustering [C]. In: Proceedings of the 6th New Zealand Computer Science Research Student Conference. 2008.
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