A Pseudo-Supervised Approach to Improve a Recommender Based on Collaborative Filtering

Abstract

This PhD Thesis develops an optimal recommender. First of all, users accessing to a Web site are clustered. If a user belongs to a cluster, the system offers services which are usually accessed by users from the same cluster in a collaborative filtering scheme. A novel approach based on a users simulator and a dynamic recommendation system is proposed. The simulator is used to create the situations that one can find in a Web site. Introduction of dynamics in the recommender allows to change the clusters and in turn, the decisions which are taken. Since the system is based both on supervised and unsupervised learning whose borders are not too clear in our approach, we talk about a pseudo-supervised learning.

This work has been partially supported by the Spanish Science and Technology Ministry project FIT-070000-2001-663, by the Valencian Culture, Education and Science Council project CTIDIA-2002-166 and by the University of Valencia project UV 01-15. I want to express my thanks to Drs. Emilio Soria-Olivas and Gustavo Camps-Valls for their direction and advices. I also want to express my thanks to Tissat, S.A., iSUM Department, http://www.tissat.es/, for its collaboration and technical support.