Comparisons instead of ratings: Towards more stable preferences

Proceedings - 2011 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2011

Volumn 1, Issue , 2011, Pages 451-456

  Jones, Nicolas ^a   Brun, Armelle ^a   Boyer, Anne ^a  

^a LORIA   (France)

Author keywords

[No Author keywords available]

Indexed keywords

INFORMATION OVERLOADS; PERSONALIZATION SERVICES; PERSONALIZATION SYSTEMS; RELIABILITY AND STABILITY; USER MODELING;

USER INTERFACES;

EID: 80155135635     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WI-IAT.2011.13     Document Type: Conference Paper

References (18)

1. AMATRIAIN, X., LATHIA, N., PUJOL, J., HAEWOON, K., AND OLIVER, N. The wisdom of the few. In Proc. of the SIGIR Conf. (2009).
2. AMATRIAIN, X., PUJOL, J. M., AND OLIVER, N. I like it i like it not: Evaluating user ratings noise in recommender systems. In Proc. of the UMAP Conf. (2009).
3. AMOO, T., AND FRIEDMAN, H. Do numeric values influence subjects' responses to rating scales? Journal of Inter. Marketing and Marketing Research 26 (2001), 41-46.
4. BENNETT, J., LANNING, S., AND NETFLIX, N. The netflix prize. In KDD Cup, Wkshp in conj. with KDD (2007).
5. BRUN, A., HAMAD, A., BUFFET, O., AND BOYER, A. Towards preference relations in recommender systems. In Workshop on Preference Learning, ECML-PKDD Conf. (2010).
6. CARTERETTE, B., AND BENNETT, P. Evaluation measures for preference judgments. In Proc. of the Annual ACM SIGIR Conf. (2008), pp. 685-686.
7. CARTERETTE, B., BENNETT, P., CHICKERING, D., AND DUMAIS, S. Here or there; preference judgments for relevance. In Proc. of the ECIR Conf. (2008), pp. 16-27.
8. COSLEY, D., LAM, S., ALBERT, I., KONSTAN, J., AND RIEDL, J. Is seeing believing?: how recommender system interfaces affect users' opinions. In Proc. of IUI (2003).
9. DAVE, K., LAWRENCE, S., AND PENNOCK, D. Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proc. of the Int. WWW Conf. (2003).
10. FISCHER, M., AND RAMAN, A. The new science of retailing: how analytics are transforming the supply chain and improving performance. Harvard Business Press, 2010.
11. JONES, N., BRUN, A., AND BOYER, A. Initial Perspectives From Preferences Expressed Through Comparisons. In Int. Conf. on Human-Computer Interaction (07 2011).
12. JONES, N., BRUN, A., BOYER, A., AND HAMAD, A. An Exploratory Work in Using Comparisons Instead of Ratings. In Proc. of Int. Conf. on Electronic Commerce and Web Technologies (EC-Web11) (2011).
13. LOUSAME, F. P., AND ŚANCHEZ, E. Web Personalization in Intelligent Environments. september 2009, ch. A Taxonomy of Collaborative-Based Recommender Systems, pp. 81-117.
14. MCGEACHIE, M. The Local Geometry of Multiattribute Tradeoff Preferences. PhD thesis, MIT, 2007.
15. PRESTON, C., AND COLMAN, A. Optimal number of response categories in rating scales: reliability, validity, discriminating power, and respondent preferences. Acta Psychologica 104, 1 (2000), 1-15.
16. RESNICK, P., IACOVOU, N., SUCHAK, M., BERGSTORM, P., AND RIEDL, J. GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In Proc. of ACM CSCW (1994), pp. 175-186.
17. SCHAFER, B., FRANKOWSKI, D., HERLOCKER, J., AND SEN, S. The Adaptive Web. Methods and Strategies of Web Personalization. Springer, 2007, ch. Collaborative Filtering Recommender Systems, pp. 291-324.
18. WESTERMANN, R., SPIES, K., STAHL, G., AND HESSE, F. W. Relative effectiveness and validity of mood induction procedures: a meta-analysis. Eur. J. Soc. Psychol. 26, 4 (1996), 557-580.