A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-Commerce

Expert Systems with Applications

Volumn 28, Issue 1, 2005, Pages 67-77

  Yu, Li ^a,b   Liu, Lu ^b   Li, Xuefeng ^b  

^a RENMIN UNIVERSITY OF CHINA   (China)

^b BEIHANG UNIVERSITY   (China)

Author keywords

Collaborative filtering; E Commerce; Personalization; Recommender systems

Indexed keywords

ALGORITHMS; COMPACT DISKS; DATA PROCESSING; DATABASE SYSTEMS; KNOWLEDGE ENGINEERING; ONLINE SYSTEMS; WEBSITES;

COLLABORATIVE FILTERING; KNOWLEDGE DISCOVERY TECHNIQUES; PERSONALIZATION; RECOMMENDER SYSTEMS;

ELECTRONIC COMMERCE;

EID: 9244231155     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2004.08.013     Document Type: Article

References (22)

1. Aggarwal, C. C., Wolf, J. L., Wu, K.-L., & Yu, P. S. (1999). Horting hatches an egg: A new graph-theoretic approach to collaborative filtering. Knowledge Discovery and Data Mining , 201-212.
2. Balabanovic, M., & Shoham, Y. (1997). FAB: Content-based collaborative recommendation. Communications of the ACM, 40(3).
3. Basu, C., Hirsh, H., & Cohen, W. (1998). Recommendation as classification: Using social and content-based information in recommendation Recommender system workshop '98 pp. 11-15.
4. Billsus, D., & Pazzani, M. (1998). Learning collaborative information filters Proceedings of the international conference on machine learning (Madison, WI). Los Altos: Morgan Kaufmann Publishers.
5. Breese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering Proceedings of the 14th conference on Uncertainly in Artificial Intelligence pp. 43 C52.
6. Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information apestry. Communications of the ACM, 35, 12.
7. Good, N., Schafer, J. B., Konstan, J. A., Borchers, A., Sarwar, B., Herlocker, J., & Riedl, J. (1999). Combining collaborative filtering with personal agents for better recommendations Proceedings of the sixteenth national conference on artificial intelligence (AAAI-99) pp. 439-446.
8. Hofmann, T. (2001). Learning what people (don't) want Proceedings of the European conference on machine learning.
9. Hill, W., Stead, L., Rosenstein, M., & Furnas, G. (1995). Recommending and evaluating choices in a virtual community of use CHI '95: Conference proceedings on human factors in computing systems, Denver, CO pp. 194-201.
10. Kitts, B., Freed, D., & Vrieze, M. (2000). Cross-sell: A fast promotion-tunable customer-item recommendation method based on conditional independent probabilities Proceedings of ACM SIGKDD international conference pp. 437-446.
11. Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L., & Riedl, J. (1997). GroupLens: Applying collaborative filtering to Usenet news. Communications of the ACM, 40(3), 77-87.
12. McJones, P. (1997). EachMovie collaborative filtering data set. DEC Systems Research Center. http://www.research.digital.com/SRC/each-movie/.
13. Mild, A., & Natter, M. A critical view on recommendation systems. Working paper series. Vienna University of Economics and Business Administration, Austria, http://www.wu-wien.ac.at/am.
14. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). GroupLens: An open architecture for collaborative filtering of netnews Proceedings of the conference on computer supported cooperative work, Chapel Hill, NC pp. 175-186.
15. Resnick, & Varian (1997). Recommender systems. Communications of the ACM, 40(3), 56 C58.
16. Sarwar, B. M., Konstan, J. A., Borchers, A., Herlocker, J., Miller, B., & Riedl, J. (1998). Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system Proceedings of CSCW '98, Seattle, WA.
17. Sarwar, B. M., Karypis, G., Konstan, J. A., & Riedl, J. (2000). Analysis of recommendation algorithms for E-Commerce Proceedings of the ACM EC'00 conference, Minneapolis, MN pp. 158-167.
18. Schafer, J. B., Konstan, J., & Riedl, J. (1999). Recommender systems in E-Commerce EC '99: Proceedings of the first ACM conference on electronic commerce, Denver, CO pp. 158-166.
19. Schafer J. B., Konstan J. A., & Riedl J. (2001). E-Commerce recommendation applications. University of Minnesota.
20. Shardanand, U., & Maes, P. (1995). Social information filtering: Algorithms for automating word of mouth Proceedings of the ACM CHI conference (CHI95).
21. Terveen, L., Hill, W., Amento, B., McDonald, D., & Creter, J. (1997). PHOAKS: A system for sharing recommendations. Communications of the ACM, 40(3), 59-62.
22. Wolf, J., Aggarwal, C., Wu, K., & Yu, P. (1999). Horting hatches and egg: A new graph-theoretic approach to collaborative filtering Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining.