Enhancing accuracy of recommender system through adaptive similarity measures based on hybrid features

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

Volumn 5991 LNAI, Issue PART 2, 2010, Pages 1-10

  Anand, Deepa ^a   Bharadwaj, Kamal K ^a  

^a JAWAHARLAL NEHRU UNIVERSITY   (India)

Author keywords

Collaborative Filtering; Hybrid User Features; Learning Similarity; Recommender Systems

Indexed keywords

COLLABORATIVE FILTERING; COLLABORATIVE FILTERING TECHNIQUES; DATA ENVIRONMENT; DEMOGRAPHIC DATA; FEATURE SIMILARITIES; FILTERING METHOD; HYBRID FEATURES; LEARNING SIMILARITY; RECOMMENDER SYSTEMS; SIMILARITY MEASURE; USER FEATURE;

DATABASE SYSTEMS; USER INTERFACES;

SIGNAL FILTERING AND PREDICTION;

EID: 77956498008     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-12101-2_1     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 Transaction on Knowledge and Data Engineering 17(6), 734-749 (2005)
2. Al-Shamri, M.Y.H., Bharadwaj, K.K.: Fuzzy-Genetic Approach to Recommender System Based on a Novel Hybrid User Model. Expert Systems with Applications 35(3), 1386-1399 (2008)
3. Bharadwaj, K.K., Al-Shamri, M.Y.H.: Fuzzy Computational Models for Trust and Reputation systems. Electronic Commerce Research and Applications 8(1), 37-47 (2009)
4. Bell, R.M., Koren, Y., Volinsky, C.: Modeling Relationships at Multiple Scales to Improve Accuracy of Large Recommender Systems. In: 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 95-108. ACM, New York (2007)
5. Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: 14th annual conference on uncertainty in artificial intelligence, pp. 43-52. Morgan Kaufmann, San Fransisco (1998)
6. Burke, R.: Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction 12, 331-370 (2002)
7. Candillier, L., Meyer, F., Fessant, F.: Designing Specific Weighted Similarity Measures to Improve Collaborative Filtering Systems. In: Perner, P. (ed.) ICDM 2008. LNCS (LNAI), vol.5077, pp. 242-255. Springer, Heidelberg (2008)
8. Cao, B., Sun, J., Wu, J., Yang, Q., Chen, Z.: Learning Bidirectional Similarity for Collaborative Filtering. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part I. LNCS (LNAI), vol.5211, pp. 178-194. Springer, Heidelberg (2008)
9. Cheung, K., Tian, L.F.: Learning User Similarity and Rating Style for Collaborative Recommendation. Information Retrieval 7(3-4), 395-410 (2004)
10. De Jong, K.A.: Learning with genetic algorithms: An overview. Machine Language 3(2-3), 121-138 (1988)
11. Jin, R., Chai, J.Y., Si, L.: An automatic weighting scheme for collaborative filtering. In: 27th annual international ACM SIGIR conference on research and development in information retrieval, pp. 337-344. ACM, New York (2004)
12. Ma, H., King, I., Lyu, M.R.: Effective missing data prediction for collaborative filtering. In: 30th annual international ACM SIGIR conference on research and development in information retrieval, pp. 39-46. ACM, New York (2007)
13. Pazzani, M.J.: A Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligence Review 13(5-6), 393-408 (1999)
14. Torres, R.S., Falcão, A.X., Zhang, B., Fan, W., Fox, E.A., Gonçalves, M.A., Calado, P.: A new framework to combine descriptors for content-based image retrieval. In: 14th ACM international conference on Information and knowledge management, pp. 335-336. ACM, New York (2005)
15. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: An Open Architecture for Collaborative Filtering of Netnews. In: ACM CSCW 1994 Conference on Computer-Supported Cooperative Work, pp. 175-186. ACM, New York (1994)
16. Stahl, A., Gabel, T.: Using evolution programs to learn local similarity measures. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS, vol.2689, pp. 537-551. Springer, Heidelberg (2003)