Evaluating the diversity of top-N recommendations

Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI

Volumn , Issue , 2009, Pages 457-460

  Zhang, Mi ^a,b   Hurley, Neil ^a  

^a UNIVERSITY COLLEGE DUBLIN   (Ireland)

^b FUDAN UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

END USERS; RECOMMENDATION ALGORITHMS;

ARTIFICIAL INTELLIGENCE;

EID: 77949500460     PISSN: 10823409     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICTAI.2009.46     Document Type: Conference Paper

References (6)

1. J. L.Herlocker, J. A.Konstan, L. G.Terveen, and J. T.Riedl. Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst., 22(1):5-53, 2004.
2. B. Mobasher, R. Burke, and J. Sandvig. Model-based collaborative filtering as a defense against profile injection attacks. In Proceedings of the 21st National Conference on Artificial Intelligence, 2006.
3. R. P., I. N., S. M., B. P., and J.Riedl. Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the ACM Conference on Computer Supported Cooperative Work.
4. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International WorldWideWeb Conference, page 1065, 2001.
5. B. Smyth and P. McClave. Similarity vs. diversity. In Proceedings of the 4th International Conference on Case-Based Reasoning, pages 347-361, 2001.
6. C. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen. Improving recommendation lists through topic diversi-fication. In Proceedings of the 14th International Conference on World Wide Web.