Measuring predictive capability in collaborative filtering

Authors:
Luis M. de Campos;Juan M. Fernandez-Luna;Juan F. Huete;Miguel A. Rueda-Morales
Affiliations:
CITIC-UGR, University of Granada, Granada, Spain;CITIC-UGR, University of Granada, Granada, Spain;CITIC-UGR, University of Granada, Granada, Spain;CITIC-UGR, University of Granada, Granada, Spain
Venue:
Proceedings of the third ACM conference on Recommender systems
Year:
2009

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Abstract

This paper presents a new memory-based approach to Collaborative Filtering where the neighbors of the active user will be selected taking into account their predictive capability. Our hypothesis is that if a user was good at predicting the past ratings, then his/her predictions will be also helpful to recommend ratings in the future. The predictive capability of a user will be measured using two different criteria: The first one which is based on the likelihood of the active user's rating and the second one tries to minimize the error obtained using his/her predictions. We show our experimental results using standard data sets.