A pseudo-supervised approach to improve a recommender based on collaborative filtering

Authors:
José D. Martín-Guerrero
Affiliations:
University of Valencia, Spain
Venue:
UM'03 Proceedings of the 9th international conference on User modeling
Year:
2003

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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.