Measuring predictive capability in collaborative filtering

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. Categories and Subject Descriptors H.3 [Information Storage and Retrieval]: General General Terms Algorithms, Theory Keywords Probabilistic Reasoning, Collaborative Filtering, Recommender System

• Type: Conference
• Year: 2009
• Where: RECSYS
• Authors: Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Miguel A. Rueda-Morales
• Keywords: Active User, Collaborative Filtering, Control Systems, Predictive Capability, RECSYS 2009