The task of guiding in adaptive recommender systems

Expert Systems with Applications

Volumn 36, Issue 2 PART 1, 2009, Pages 1972-1977

  Hernández del Olmo, Félix ^a   Gaudioso, Elena ^a   Martin, Eduardo H ^a  

^a UNIVERSIDAD NACIONAL DE EDUCACIÓN A DISTANCIA   (Spain)

Author keywords

Adaptive web based systems; Interactive web based systems; Recommender systems; User models; User profiles

Indexed keywords

COLLABORATIVE FILTERING; LEARNING ALGORITHMS; LEARNING SYSTEMS; WEBSITES;

ADAPTIVE WEB BASED SYSTEMS; CONTENT-BASED; INTERACTIVE WEB BASED SYSTEMS; TASK-NEEDS; USER MODELS; USER PROFILE;

RECOMMENDER SYSTEMS;

EID: 56349157975     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2007.12.070     Document Type: Article

References (15)

1. Adomavicius G., and Tuzhilin A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17 6 (2005) 734-749
2. Balabanovic M., and Shoham Y. Fab: Content-based, collaborative recommendation. Communications of the ACM 40 3 (1997) 66-72
3. Breese J., Heckerman D., and Kadie C. Empirical analysis of predictive algorithms for collaborative filtering. Uncertainty in Artificial Intelligence. Proceedings of the 14th Conference (1998), Morgan Kaufman 43-52
4. Burke R. Hybrid recommender systems. User Modeling and User-Adapted Interaction 12 4 (2002) 331-370
5. Herlocker J.L., Konstan J.A., Borchers A., and Riedl J. An algorithmic framework for performing collaborative filtering. SIGIR'99: Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval (1999), ACM Press, New York, NY, USA 230-237
6. Herlocker J.L., Konstan J.A., Terveen L.G., and Riedl J.T. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems 22 1 (2004) 5-53
7. Hernandez del Olmo, F., Gaudioso, E., Boticario, J. G., 2005. Evaluating the intrusion cost of recommending in recommender systems. In User modeling 2005, 10th international conference (pp. 342-346).
8. Langley P. User modeling in adaptive interfaces. Proceedings of the seventh international conference on user modeling (1999), Springer, Banff, Alberta 357-370
9. Linden G., Smith B., and York J. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing 7 1 (2003) 76-80
10. Pazzani M.J., Muramatsu J., and Billsus D. Learning and revising user profiles: The identification of interesting web sites. Machine Learning 27 (1997) 313-331
11. Provost F., Fawcett T., and Kohavi R. The case against accuracy estimation for comparing induction algorithms. Proceedings of the 15th international conference on machine learning (1998), Morgan Kaufmann, San Francisco, CA 445-453
12. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J., 1994. Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the conference on computer supported cooperative work.
13. Schwab I., and Kobsa A. Adaptivity through unobstrusive learning. KI 3 5-9 (2002)
14. Sutton R.S., and Barto A.G. Reinforcement learning: An introduction (1998), MIT Press
15. Webb G., Pazzani M.J., and Billsus D. Machine learning for user modeling. User Modeling and User-Adapted Interaction 11 (2001) 19-29