Predicting most rated items in weekly recommendation with temporal regression

ACM International Conference Proceeding Series

Volumn , Issue , 2010, Pages 24-27

  Brenner, Antoine ^a   Pradel, Bruno ^a   Usunier, Nicolas ^a   Gallinari, Patrick ^a  

^a UNIVERSITÉ PIERRE ET MARIE CURIE   (France)

Author keywords

Contextual recommendation; Recommender systems

Indexed keywords

CHRISTMAS; CONTEXTUAL RECOMMENDATION; PERSONALIZED RECOMMENDATION; RECOMMENDER SYSTEMS; REGRESSION TECHNIQUES; TEST PERFORMANCE; TIME-DEPENDENT;

RATING;

FORECASTING;

EID: 78650129677     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1869652.1869656     Document Type: Conference Paper

References (6)

1. L. Batrunas and X. Amatriain. Towards Time-Dependent Recommendation Based on Implicit Feedback. In Proc. of RecSys'09 Workshop on Context-Aware Rec. Sys., 2009.
2. Y. Koren. Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model. In Proc. of the 14th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, pages 426-434, 2008.
3. Y. Koren. Collaborative filtering with temporal dynamics. Commun. ACM, 53(4):89-97, 2010.
4. B. Marlin. Collaborative Filtering: A Machine Learning Perspective. Master's thesis, University of Toronto, 2004.
5. A. Said, S. Berkovsky and E. W. De Luca. Putting Things in Context: Challenge on Context-Aware Movie Recommendation In CAMRa2010: Proc. of the RecSys'10 Challenge on Context-aware Movie Recommendation, 2010.
6. M. Weimer, A. Karatzoglou, and A. J. Smola. Improving maximum margin matrix factorization. Machine Learning, 72(3):263-276, 2008.