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.
['Luis M. de Campos', 'Juan M. Fernández-Luna', 'Juan F. Huete', 'Miguel A. Rueda-Morales']
Measuring predictive capability in collaborative filtering