Please wait a minute...
New Technology of Library and Information Service  2016, Vol. 32 Issue (6): 73-79    DOI: 10.11925/infotech.1003-3513.2016.06.09
Orginal Article Current Issue | Archive | Adv Search |
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
Download: PDF(923 KB)   HTML ( 57
Export: BibTeX | EndNote (RIS)      

[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:     OR

[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.
[1] Jie Li,Fang Yang,Chenxi Xu. A Personalized Recommendation Algorithm with Temporal Dynamics and Sequential Patterns[J]. 数据分析与知识发现, 2018, 2(7): 72-80.
[2] Daoping Wang,Zhongyang Jiang,Boqing Zhang. Collaborative Filtering Algorithm Based on Gray Correlation Analysis and Time Factor[J]. 数据分析与知识发现, 2018, 2(6): 102-109.
[3] Yong Wang,Yongdong Wang,Huifang Guo,Yumin Zhou. Measuring Item Similarity Based on Increment of Diversity[J]. 数据分析与知识发现, 2018, 2(5): 70-76.
[4] Lingfeng Hua,Gaoming Yang,Xiujun Wang. Recommending Diversified News Based on User’s Locations[J]. 数据分析与知识发现, 2018, 2(5): 94-104.
[5] Jun Hou,Kui Liu,Qianmu Li. Classification Recommendation Based on ESSVM[J]. 数据分析与知识发现, 2018, 2(3): 9-21.
[6] Fuliang Xue,Junling Liu. Improving Collaborative Filtering Recommendation Based on Trust Relationship Among Users[J]. 数据分析与知识发现, 2017, 1(7): 90-99.
[7] Xingxin Qin,Rongbo Wang,Xiaoxi Huang,Zhiqun Chen. Slope One Collaborative Filtering Algorithm Based on Multi-Weights[J]. 数据分析与知识发现, 2017, 1(6): 65-71.
[8] Li Daoguo,Li Lianjie,Shen Enping. New Collaborative Filtering Recommendation Algorithm Based on User Rating Time[J]. 现代图书情报技术, 2016, 32(9): 65-69.
[9] Tan Xueqing,Zhang Lei,Huang Cuicui,Luo Lin. A Collaborative Filtering and Recommendation Algorithm Using Trust of Domain-Experts and Similarity[J]. 现代图书情报技术, 2016, 32(7-8): 101-109.
[10] Ma Li. Collaborative Filtering Recommendation Method Based on User Learning Tree[J]. 现代图书情报技术, 2016, 32(4): 72-80.
[11] Shuhao Jiang, Liyi Zhang, Zhixin Zhang. New Collaborative Filtering Algorithm Based on Relative Similarity[J]. 数据分析与知识发现, 2016, 32(12): 44-49.
[12] Wu Yingliang, Yao Huaidong, Li Cheng'an. An Improved Collaborative Filtering Recommendation Algorithm with Indirect Trust Relationship[J]. 现代图书情报技术, 2015, 31(9): 38-45.
[13] Zhu Ting, Qin Chunxiu, Li Zuhai. Research on Collaborative Filtering Personalized Recommendation Method Based on User Classification[J]. 现代图书情报技术, 2015, 31(6): 13-19.
[14] Gao Huming, Zhao Fengyue. A Hybrid Recommendation Method Combining Collaborative Filtering and Content Filtering[J]. 现代图书情报技术, 2015, 31(6): 20-26.
[15] Ying Yan, Cao Yan, Mu Xiangwei. A Hybrid Collaborative Filtering Recommender Based on Item Rating Prediction[J]. 现代图书情报技术, 2015, 31(6): 27-32.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938