Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems

RecSys'09 - Proceedings of the 3rd ACM Conference on Recommender Systems

Volumn , Issue , 2009, Pages 265-268

  Panniello, Umberto ^a   Tuzhilin, Alexander ^b   Gorgoglione, Michele ^a   Palmisano, Cosimo ^c   Pedone, Anto ^a  

^a POLITECNICO DI BARI   (Italy)

^b NEW YORK UNIVERSITY   (United States)

^c Aizoon Consulting   (Italy)

Author keywords

Collaborative filtering; Post filtering; Pre filtering; Recommender systems

Indexed keywords

COLLABORATIVE FILTERING; CONTEXT-AWARE; CONTEXT-AWARE RECOMMENDER SYSTEMS; CONTEXTUAL MODELING; EXPERIMENTAL COMPARISON; POST-FILTERING; PRE-FILTERING; RECOMMENDER SYSTEMS;

SIGNAL FILTERING AND PREDICTION;

EID: 72249085351     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1639714.1639764     Document Type: Conference Paper

References (9)

1. Adomavicius, G., Sankaranarayanan, R.,Sen, S., and Tuzhilin, A. 2005. Incorporating contextual information in recommender systems using a multidimensional approach. ACM T. Inform. Syst. 23, 1 (2005), 103-145.
2. Adomavicius, G., and Tuzhilin, A. 2001. Multidimensional recommender systems: a data warehousing approach. In Proc. of WECOM Conference, LNCS, 2232 Springer.
3. Adomavicius, G., and Tuzhilin, A. 2008. Context-Aware Recommender Systems. ACM RecSys Tutorial, 2008.
4. Anand, A., and Mobasher, B. 2007. Contextual Recommendation. In From Web to Social Web: Discovering and Deploying User and Content Profiles (September 2006), Springer, Berlin, 142-160.
5. Bettman, J.R., Luce, M.F, and Payne, J.W. 1998. Constructive consumer choice processes. JCR, 25(3).
6. Kachigan, S.C. 1986 Statistical Analysis. Radius Press.
7. th DELOS Workshop.
8. Oku, K., Nakajima, S., Miyazaki, J. and Uemura, S., 2006. Context-aware SVM for context-dependent information recommendation.7th Intl. Conf. on Mobile Data Managmnt
9. Palmisano, C., Tuzhilin, A., and Gorgoglione, M. 2008. Using Context to Improve Predictive Models of Customers in Personalization Applications. IEEE TKDE, 20(11).