A similarity measure for collaborative filtering with implicit feedback

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

Volumn 4682 LNAI, Issue , 2007, Pages 385-397

  Lee, Tong Queue ^a   Park, Young ^b   Park, Yong Tae ^c  

^a Dongyang University   (South Korea)

^b BRADLEY UNIVERSITY   (United States)

^c Seoul National University   (South Korea)

Author keywords

Collaborative filtering; E commerce; Implicit feedback; Recommendation accuracy; Recommender system; Similarity measure

Indexed keywords

ELECTRONIC COMMERCE; FEEDBACK; PROBLEM SOLVING; USER INTERFACES;

COLLABORATIVE FILTERING; IMPLICIT FEEDBACK; RECOMMENDATION ACCURACY; RECOMMENDER SYSTEMS; SIMILARITY MEASURES;

COMPUTER SUPPORTED COOPERATIVE WORK;

EID: 38049012939     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74205-0_43     Document Type: Conference Paper

References (11)

1. Resnick, P., lacovou, N., Suchak, M., Bergstrom, P., Riedl, J. GroupLens: An Open Architecture for Collaborative Filtering of Netnews. Proceedings of CSCW '94 (1994) 175-186
2. Linden, G., Smith, B., York, J.: Amazon.com Recommendations. Item-to-Item Collaborative Filtering. IEEE Internet Computing (2003)
3. Melville, P., Mooney, R. J., Nagarajan, R.: Content-Boosted Collaborative Filtering for Improved Recommendations. Proceedings of Eighth National Conference an Artificial Intelligence (2002) 187-192
4. Caglayan, A., Snorrason, M., Jacoby,J., Mazzu, J.,Jones, R., Kumar, K.: Learn Sesame - A Learning Agent Engine. Applied Artificial Intelligence. Vol. 11 (1997) 393-412
5. Middleton, S.E., Shadbolt, N.R., de Roure, D.C.: Oncological User Profiling in Reeommender Systems. ACM Trans. Information Systems. Vol. 22. no. 1 (2004) 54-88
6. Oard, D.W., Kim, J.: Implicit Feedback for Reeommender Systems. Proceedings of Reeommender Systems 1998 Workshop (1998)
7. Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and Metrics for Cold-Start Recommendations. Proceedings of Ann. Int'l ACM SIGIR Conf. (2002)
8. Mobasher, B., Dai, H., Luo, T., Sun, Y., Zhu, J.: Automatic Personalization Based on Web Usage Mining. Communications of the ACM. Vol. 43(8) (2000) 142-151
9. Anderson, C. R., Domingos, P., Weld, D. S.: Personalizing Web Sites for Mobile Users. Proceedings of the 10th Conference on the World Wide Web (2001)
10. Tang, T. Y., Winoto, P., Chan, K. C. C.: Scaling Down Candidate Sets Based on the Temporal Feature of Items for Improved Hybrid Recommendations. Intelligent Techniques in Web Personalization. LNAI 3169 (2003) 169-185
11. Ding Y., Li, X. Orlowska M.: Recency-Based Collaborative Filtering, Australian Computer Science Communications. Vol. 28 No 2. Australasian Database Conference. ACM Digital Library (2006) 99-107