|
|
|
| Recommendation Algorithm Based on User-Interest-Item Tripartite Graph |
| ZHANG Yan-Mei, WANG Lu, CAO Huai-Hu, MAO Guo-Jun |
| School of Information, Central University of Finance and Economics, Beijing 100081 |
|
|
|
|
Abstract Since most of the existing personalized recommendation algorithms pursue a higher accuracy, their performance is affected by the problem of user interest over-specialization. An algorithm is proposed to fully mine and use the implicit user interest information for recommendation. The probabilistic topic model is adopted to extract user interest distribution, and the weighted tripartite graph of user-interest-item is generated. Then the user item resource value is allocated by material diffusion algorithm in user-interest and interest-item bipartite graphs respectively, and the Top-K recommendation list is generated according to the rank of item resource values. Experimental results on Movielens datasets show the proposed algorithm relieves the problem of user interest over-specialization. Meanwhile the recommendation accuracy is improved .
|
|
Received: 10 September 2014
|
|
|
|
|
|
[1] Li X, Chen H C. Recommendation as Link Prediction in Bipartite Graphs: A Graph Kernel-Based Machine Learning Approach. Decision Support Systems, 2013, 54(2): 880-890
[2] Liu J G, Zhou T, Wang B H. Personalized Recommendation System Research Process. Progress in Natural Science, 2009, 19(1): 1-15 (in Chinese)
(刘建国,周 涛,汪秉宏.个性化推荐系统的研究进展.自然科学进展, 2009, 19(1): 1-15)
[3] Zhou T, Ren J, Medo M, et al. Bipartite Network Projection and Personal Recommendation. Physical Review E, 2007. DOI: 10.
1103/PhysRevE.76.046115
[4] Gemmell J, Schimoler T, Ramezani M, et al. Improving Folkrank with Item-Based Collaborative Filtering [EB/OL]. [2014-08-30]. http:// ceur-ws.org/Vol-532/paper3.pdf
[5] Brin S, Page L. The Anatomy of a Large-Scale Hypertextual Web Search Engine [EB/OL]. [2014-08-20]. http:// ilpubs.stanford. edu: 8090/361/1/1998-8.pdf
[6] Zhang Z K, Zhou T, Zhang Y C. Personalized Recommendation via Integrated Diffusion on User-Item-Tag Tripartite Graphs. Physica A: Statistical Mechanics and Its Applications, 2010, 389(1): 179-186
[7] Song Y, Zhang L, Giles C L. Automatic Tag Recommendation Algorithms for Social Recommender Systems. ACM Trans on the Web(TWEB), 2011. DOI: 10.1145/1921591.1921595
[8] Shepitsen A, Gemmell J, Mobasher B, et al. Personalized Recommendation in Social Tagging Systems Using Hierarchical Clustering // Proc of the ACM Conference on Recommender Systems. Lausanne, Switzerland, 2008: 259-266
[9] Krestel R, Fankhauser P. Personalized Topic-Based Tag Recommendation. Neurocomputing, 2012, 76(1): 61-70
[10] Durao F, Dolog P. A Personalized Tag-Based Recommendation in Social Web Systems // Proc of the Workshop on Adaptation and Personalization for Web 2.0. Trento, Italy, 2009: 40-49
[11] Symeonidis P, Nanopoulos A, Manolopoulos Y. A Unified Framework for Providing Recommendations in Social Tagging Systems Based on Ternary Semantic Analysis. IEEE Trans on Knowledge and Data Engineering, 2010, 22(2): 179-192
[12] García-Crespo , Colomo-Palacios R, Gómez-Berbís J M, et al. SEMO: A Framework for Customer Social Networks Analysis Based on Semantics. Journal of Information Technology, 2010, 25(2): 178-188
[13] Baruzzo A, Dattolo A, Pudota N, et al. Recommending New Tags Using Domain-Ontologies // Proc of the IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technologies. Milan, Italy, 2009, III: 409-414
[14] Fu W T, Dong W. Collaborative Indexing and Knowledge Exploration: A Social Learning Model. IEEE Intelligent Systems, 2010, 27(1): 39-46
[15] Leginus M, Dolog P, emaitis V. Improving Tensor Based Recommenders with Clustering // Proc of the 20th International Conference on User Modeling, Adaptation, and Personalization. Montreal, Canada, 2012: 151-163
[16] Blattner M, Zhang Y C, Maslov S. Exploring an Opinion Network for Taste Prediction: An Empirical Study. Physica A: Statistical Mechanics and Its Applications, 2007, 373: 753-758
[17] Goldberg D, Nichols D, Oki B M, et al. Using Collaborative Fil-tering to Weave an Information Tapestry. Communications of the ACM, 1992, 35(12): 61-70 |
|
|
|
2015, Vol. 28 
