Towards Preference Relations in Recommender Systems - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2010

Towards Preference Relations in Recommender Systems

Ahmad Hamad
  • Function : Author
  • PersonId : 864744
Olivier Buffet
Anne Boyer


Collaborative ltering-based recommender systems exploit user preferences about items to provide them with recommendations. These preferences are generally ratings. However, choosing a rating is no easy task for any user; the rating scale is usually reduced and the rating values given by the users may be in uenced by many factors. The rat- ings are thus not completely trustworthy. This paper is a rst attempt at studying the expression of preferences in collaborative ltering under the form of preference relations instead of ratings. When using preference relations, users are asked to compare pairs of resources. We propose new measures to compute recommendations using preference relations. First experiments have been conducted on a state of the art corpus of the rec- ommender systems domain and show that this new approach compares with, and in some cases improves the classical one.
No file

Dates and versions

inria-00523496 , version 1 (05-10-2010)


  • HAL Id : inria-00523496 , version 1


Armelle Brun, Ahmad Hamad, Olivier Buffet, Anne Boyer. Towards Preference Relations in Recommender Systems. Workshop on Preference Learning (PL2010) in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML-PKDD, Eyke Hüllermeier and Johannes Fürnkranz, Sep 2010, Barcelona, Spain. ⟨inria-00523496⟩
229 View
0 Download


Gmail Facebook X LinkedIn More