Adapting to natural rating acquisition with combined active learning strategies

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

Volumn 7661 LNAI, Issue , 2012, Pages 254-263

  Elahi, Mehdi ^a   Ricci, Francesco ^a   Rubens, Neil ^b  

^a FREE UNIVERSITY OF BOZEN BOLZANO   (Italy)

^b UNIVERSITY OF ELECTRO COMMUNICATIONS   (Japan)

Author keywords

active learning; combined strategies; rating elicitation; Recommender systems

Indexed keywords

ACTIVE LEARNING; COMBINED-STRATEGIES; NATURALLY OCCURRING;

INTELLIGENT SYSTEMS; MERGERS AND ACQUISITIONS; RECOMMENDER SYSTEMS;

RATING;

EID: 84870908261     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-34624-8_30     Document Type: Conference Paper

References (11)

1. Burke, R.: Evaluating the dynamic properties of recommendation algorithms. In: Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys 2010, pp. 225-228. ACM, New York (2010)
2. Carenini, G., Smith, J., Poole, D.: Towards more conversational and collaborative recommender systems. In: Proceedings of the 2003 International Conference on Intelligent User Interfaces, Miami, FL, USA, January 12-15, pp. 12-18 (2003)
3. Elahi, M., Repsys, V., Ricci, F.: Rating Elicitation Strategies for Collaborative Filtering. In: Huemer, C., Setzer, T. (eds.) EC-Web 2011. LNBIP, vol. 85, pp. 160-171. Springer, Heidelberg (2011)
4. Golbandi, N., Koren, Y., Lempel, R.: Adaptive bootstrapping of recommender systems using decision trees. In: Proceedings of the Forth International Conference on Web Search and Web Data Mining, WSDM 2011, Hong Kong, China, February 9-12, pp. 595-604 (2011)
5. Koren, Y., Bell, R.: Advances in collaborative filtering. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 145-186. Springer (2011)
6. Lathia, N.K.: Evaluating Collaborative Filtering Over Time. PhD thesis, University College London (June 2010)
7. Rashid, A.M., Albert, I., Cosley, D., Lam, S.K., Mcnee, S.M., Konstan, J.A., Riedl, J.: Getting to know you: Learning new user preferences in recommender systems. In: Proceedings of the 2002 International Conference on Intelligent User Interfaces, IUI 2002, pp. 127-134. ACM Press (2002)
8. Rashid, A.M., Karypis, G., Riedl, J.: Learning preferences of new users in recommender systems: an information theoretic approach. SIGKDD Explor. Newsl. 10, 90-100 (2008)
9. Rubens, N., Kaplan, D., Sugiyama, M.: Active learning in recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 735-767. Springer (2011)
10. Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and metrics for cold-start recommendations. In: SIGIR 2002: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 253-260. ACM, New York (2002)
11. Zhou, K., Yang, S.-H., Zha, H.: Functional matrix factorizations for cold-start recommendation. In: Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011, Beijing, China, July 25-29, pp. 315-324 (2011)