|
|
|
| Collaborative Filtering Recommendation Algorithm Based on Joint Nonnegative Matrix Factorization |
| HUANG Bo, YAN Xuanhui, LIN Jianhui |
| College of Mathematics and Computer Science, Fujian Normal University, Fuzhou 350117 |
|
|
|
|
Abstract To reveal the hidden relationship between complex network structures and recommend items to users more accurately, an algorithmbased on joint nonnegative matrix factorization (JNMF) is proposed. In the algorithm, user-based collaborative filtering is combined with item-based collaborative filtering. The validity and the convergence of the algorithm are presented in the appendix as well. The experimental results show that the proposed algorithm can combine user-based collaborative filtering algorithm and item-based collaborative filtering algorithm effectively, reduce the mean absolute error to some extent and improve the accuracy of recommendation.
|
|
Received: 15 September 2015
|
| Fund:Supported by Natural Science Foundation of Fujian Province (No.2013J01223) |
| About author:: (HUANG Bo, born in 1991, master student. His research interests include personalized recommendation and data mining.)(YAN Xuanhui(Corresponding author), born in 1968, Ph.D. candidate, associate professor. His research interests include computational intelligence and data mining.)(LIN Jianhui, born in 1988, master student. His research interests include personalized recommendation and data mining.) |
|
|
|
[1] ADOMAVICIUS G, TUZHILIN A. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Po-ssible Extensions. IEEE Trans on Knowledge and Data Engineering, 2005, 17(6): 734-749.
[2] SARWAR B, KARYPIS G, KONSTAN J, et al. Item-Based Colla-borative Filtering Recommendation Algorithms // Proc of the 10th International Conference on World Wide Web. New York, USA: ACM, 2001: 285-295.
[3] XUE G R, LIN C X, YANG Q, et al. Scalable Collaborative Filt-ering Using Cluster-Based Smoothing // Proc of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2005: 114-121.
[4] GONG S J. An Efficient Collaborative Recommendation Algorithm Based on Item Clustering. // LUO Q, ed. Advanced in Wireless Networks and Information Systems. Berlin, Germany: Springer-Verlag, 2010: 381-387.
[5] 王 鹏,王晶晶,俞能海.基于核方法的User-Based协同过滤推荐算法.计算机研究与发展, 2013, 50(7): 1444-1451.
(WANG P, WANG J J, YU N H. A Kernel and User-Based Co-llaborative Filtering Recommendation Algorithm. Journal of Computer Research and Development, 2013, 50(7): 1444-1451.)
[6] WANG J, DE VRIES A P, REINDERS M J T. Unifying User-Based and Item-Based Collaborative Filtering Approaches by Similarity Fusion // Proc of the 29th Annual International ACM SIGIR Confe-rence on Research and Development in Information Retrieval. New York, USA: ACM, 2006: 501-508.
[7] HU L, SONG G H, XIE Z Z, et al. Personalized Recommendation Algorithm Based on Preference Features. Tsinghua Science & Technology, 2014, 19(3): 293-299.
[8] HONDA K, OH C H, MATSUMOTO Y, et al. Exclusive Partition in FCM-Type Co-clustering and Its Application to Collaborative Fi-ltering. International Journal of Computer Science and Network Security, 2012, 12(12): 52-58.
[9] 刘建国,周 涛,汪秉宏.个性化推荐系统的研究进展.自然科学发展, 2009, 19(1): 1-15.
(LIU J G, ZHOU T, WANG B H. Research Progress of Persona-lized Recommendation System. Natural Science Development, 2009, 19(1): 1-15.)
[10] LEE D D, SEUNG H S. Algorithms for Non-negative Matrix Factorization [C/OL]. [2015-09-20]. http: // papers.nips.cc/paper/1861-algorithms-fornon-negative-matrix-factorization.pdf.
[11] LEE D D, SEUNG H S. Learning the Parts of Objects by Non-ne-gative Matrix Factorization. Nature, 1999, 401(6755): 788-791.
[12] 刘维湘,郑南宁,游屈波.非负矩阵分解及其在模式识别中的应用.科学通报, 2006, 51(3): 241-250.
(LIU W X, ZHENG N N, YOU Q B. Nonnegative Matrix Factorization and Its Application in Pattern Recognition. Chinese Science Bulletin, 2006, 51(3): 241-250.)
[13] WANG F, LI T, WANG X, et al. Community Discovery Using Nonnegative Matrix Factorization. Data Mining and Knowledge Discovery, 2011, 22(3): 493-521.
[14] CHEN G, WANG F, ZHANG C S. Collaborative Filtering Using Orthogonal Nonnegative Matrix Tri-factorization. Information Processing & Management, 2009, 45(3): 368-379.
[15] DEMPSTER A P, LAIRD N M, RUBIN D B. Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society(Methodological), 1977, 39(1): 1-38.
[16] VUCETIC S, OBRADOVIC Z. Collaborative Filtering Using a Regression-Based Approach. Knowledge and Information Systems, 2005, 7(1): 1-22.
[17] 文俊浩,舒 珊.一种改进相似性度量的协同过滤推荐算法.计算机科学, 2014, 41(5): 68-71.
(WEN J H, SHU S. Improved Collaborative Filtering Recommendation Algorithm of Similarity Measure. Computer Science, 2014, 41(5): 68-71.)
[18] WEN Y, LIU Y, CAO W. A Network-Based Recommendation Algorithm via Improved Similarity Model. Information Technology Journal, 2014, 13(5): 978-983.
[19] WANG X W, ZHANG J. SVD-Based Privacy Preserving Data Updating in Collaborative Filtering // Proc of the Word Congress on Engineering. Hong Kong, China: IAENG, 2012, II: 377-384.
[20] WANG X W, ZHANG J, LIN P P, et al. Incorporating Auxiliary Information in Collaborative Filtering Data Update with Privacy Preservation. International Journal of Advanced Computer Science and Applications, 2014, 5(4): 224-235.
[21] LUO X, ZHOU M C, XIA Y N, et al. An Efficient Non-negative Matrix-Factorization-Based Approach to Collaborative Filtering for Recommender Systems. IEEE Trans on Industrial Informatics, 2014, 10(2): 1273-1284. |
|
|
|
2016, Vol. 29 
