JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2017, Vol. 52 ›› Issue (11): 37-43.doi: 10.6040/j.issn.1671-9352.0.2017.326

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User similarity calculation method based on probabilistic matrix factorization and its recommendation application

HUANG Shu-qin1, 2, XU Yong1, WANG Ping-shui1   

  1. 1. School of Management Science and Engineering, Anhui University of Finance and Economics, Bengbu 233030, Anhui, China;
    2. School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, Anhui, China
  • Received:2017-05-30 Online:2017-11-20 Published:2017-11-17

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Abstract: The rationality of user similarity computation directly affects the effect of collaborative filtering. A method is proposed to construct a user consumption network and a calculation formula of asymmetric user similarity was defined. The initial user similarity matrix is set up based on the user asymmetric similarity values and reconstructed via probabilistic matrix factorization. And then, potential nearest neighbor mined is applied to personalized recommendation of the target user. Meanwhile, a collaborative filtering recommendation framework is put forward based on sequential behavior and probabilistic matrix factorization. The actual parameters are experimented on real-world dataset and compared with other methods. Experimental results show that the proposed method can improve the effect of collaborative filtering recommendation.

Key words: nearest neighbor, asymmetric user similarity, sequential behavior relationship, probabilistic matrix factorization

CLC Number: 

  • TP393
[1] EKSTRAND M D, RIEDL J T, KONSTAN J A. Collaborative filtering recommender systems[J]. Foundations and Trends in Human-Computer Interaction, 2010, 4(2): 81-173.
[2] LIU Qi, CHEN Enhong, XIONG Hui, et al. Enhancing collaborative filtering by user interests expansion via personalized ranking[J]. IEEE Transactions on Systems, Man and Cybernetics-part B: Cybernetics, 2012, 42(1):218-233
[3] CHOI K, SUH Y. A new similarity function for selecting neighbors for each target item in collaborative filtering[J]. Knowledge-Based Systems. 2013, 37:146-153
[4] SHI Y, LARSON M, HANJALIC A. Exploiting user similarity based on rated-item pools for improved user-based collaborative filtering[C] // Proceeding of the third ACM conference on Recommender systems(RecSys’09). New York: ACM, 2009: 125-132.
[5] BREESE J S, HECKERMAN D, KADIE C. Empirical analysis of predictive algorithms for collaborative filtering[C] // Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. San Francisco:Morgan Kaufmann, 1998: 43-52.
[6] CANDILLIER L, MEYER F, BOUllé M. Comparing state-of-the-art collaborative filtering systems[C] // Machine Learning and Data Mining in Pattern Recognition(MLDM’07). Berlin: Springerverlag Berlin, 2007: 548-562.
[7] PATRA B K, LAUNONEN R, OLLIKAINEN V, et al. A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data[J]. Knowledge-Based Systems, 2015, 82:163-177.
[8] KOUTRICA G, BERCOVITZ B, GARCIA H. FlexRecs: expressing and combining flexible recommendations[C] // Proceedings of the 35th SIGMOD International Conference on Management of Data Providence. New York: ACM, 2009: 745-757.
[9] CACHEDA F, CARNEIRO V, FERNÁNDEZ D, et al. Comparison of collaborative filtering algorithms: limitations of current techniques and proposals for scalable, high-performance recommender systems[J]. ACM Transactions on the Web(TWEB), 2011, 5(1):Article 2. Doi:10.1145/1921591.1921593.
[10] BOBADILLA J, SERRADILLA F, BERNAL J. A new collaborative filtering metric that improves the behavior of recommender systems[J]. Knowledge-Based Systems, 2010, 23:520-528.
[11] LIU HAIFENG, HU ZHENG, MIAN A, et al. A new user similarity model to improve the accuracy of collaborative filtering[J]. Knowledge-Based Systems, 2014, 56:156-166.
[12] SHARDANAND U. Social information filtering for music recommendation[D]. Boston: Massachusetts Institute of Technology, 1994.
[13] 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.
[14] PIRASTEH P, HWANG D, JUNG J J. Exploiting matrix factorization to asymmetric user similarities in recommendation systems[J]. Knowledge-Based Systems, 2015, 83:51-57.
[15] JAMALI M, ESTER M. A matrix factorization technique with trust propagation for recommendation in social networks[C] // Proceedings of the 4th ACM conference on Recommender Systems(RecSys’10). New York: ACM, 2010: 135-142.
[16] SALAKHUTDINOV R, MNIH A. Probabilistic matrix factorization[C] // International Conference on Neural Information Processing Systems(NIPS’07). Cambridge: MIT, 2007: 1257-1264.
[17] 孙光福,吴乐,刘淇,等.基于时序行为的协同过滤推荐算法[J] ,软件学报,2013,24(11):2721-2733. SUN Guangfu, WU Le, LIU Qi, et al. Recommendations based on collaborative filtering by exploiting sequential behavior[J]. Journal of Software, 2013, 24(11):2721-2733.
[1] NI Zhan, WU Qun-ying*, SHI Sheng-ta. The rate of strong consistency of nearest neighbor density  estimator for ND samples [J]. J4, 2012, 47(12): 6-9.
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