Authors:
Jean-Francois Pessiot
;
Tuong-Vinh Truong
;
Nicolas Usunier
;
Massih-Reza Amini
and
Patrick Gallinari
Affiliation:
University of Paris VI, France
Keyword(s):
Collaborative Filtering, Recommender Systems, Machine Learning, Ranking.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Business Analytics
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Information Systems
;
Industrial Applications of Artificial Intelligence
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
Up to now, most contributions to collaborative filtering rely on rating prediction to generate the recommendations. We, instead, try to correctly rank the items according to the users’ tastes. First, we define a ranking error function which takes available pairwise preferences between items into account. Then we design an effective algorithm that optimizes this error. Finally we illustrate the proposal on a standard collaborative filtering dataset. We adapted the evaluation protocol proposed by (Marlin, 2004) for rating prediction based systems to our case, where pairwise preferences are predicted instead. The preliminary results are between those of two reference rating prediction based methods. We suggest different directions to further explore our ranking based approach for collaborative filtering.