A robust multi-criteria recommendation approach with preference-based similarity and support vector machine

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7952 LNCS, Issue PART 2, 2013, Pages 385-394

  Fan, Jun ^a   Xu, Linli ^a  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

multi criteria; preference; recommendation; sparsity; support vector regression

Indexed keywords

NEURAL NETWORKS; RECOMMENDER SYSTEMS; SUPPORT VECTOR MACHINES;

AGGREGATION FUNCTIONS; DATA PREPROCESSING; DATA SPARSITY PROBLEMS; MULTI-CRITERIA; PREFERENCE; RECOMMENDATION; SPARSITY; USERS' INTERESTS;

SUPPORT VECTOR REGRESSION;

EID: 84880725288     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-39068-5_47     Document Type: Conference Paper

References (16)

1. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734-749 (2005) (Pubitemid 40860454)
2. Konstan, J.: Introduction to recommender systems: Algorithms and evaluation. ACM Transactions on Information Systems (TOIS) 22(1), 1-4 (2004)
3. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, pp. 175-186. ACM (1994)
4. Adomavicius, G., Manouselis, N., Kwon, Y.: Multi-criteria recommender systems. In: Recommender Systems Handbook, pp. 769-803 (2011)
5. Adomavicius, G., Kwon, Y.: New recommendation techniques for multicriteria rating systems. IEEE Intelligent Systems 22(3), 48-55 (2007) (Pubitemid 46883395)
6. Breese, J., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 43-52. Morgan Kaufmann Publishers Inc. (1998)
7. Tang, T., McCalla, G.: The pedagogical value of papers: a collaborative-filtering based paper recommender. Journal of Digital Information 10(2) (2009)
8. Liu, L., Mehandjiev, N., Xu, D.: Multi-criteria service recommendation based on user criteria preferences. In: Proceedings of the Fifth ACM Conference on Recom-mender Systems, pp. 77-84. ACM (2011)
9. Xin, X., Lyu, M., King, I.: Cmap: effective fusion of quality and relevance for multi-criteria recommendation. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 455-464. ACM (2011)
10. Li, Q., Wang, C., Geng, G.: Improving personalized services in mobile commerce by a novel multicriteria rating approach. In: Proceeding of the 17th International Conference on World Wide Web, pp. 1235-1236 (2008)
11. De Lathauwer, L., De Moor, B., Vandewalle, J.: A multilinear singular value decomposition. SIAM Journal on Matrix Analysis and Applications 21(4), 1253-1278 (2000) (Pubitemid 33217068)
12. Vapnik, V., Golowich, S., Smola, A.: Support vector method for function approximation, regression estimation, and signal processing. Advances in Neural Information Processing Systems, 281-287 (1997)
13. Joachims, T.: Text categorization with support vector machines: Learning with many relevant features. Machine Learning: ECML 1998, 137-142 (1998) (Pubitemid 128067178)
14. Willmott, C., Matsuura, K.: Advantages of the mean absolute error (mae) over the root mean square error (rmse) in assessing average model performance. Climate Research 30(1), 79 (2005)
15. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285-295. ACM (2001)
16. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168-177. ACM (2004) (Pubitemid 40114926)