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RECSYS
2009
ACM

Measuring predictive capability in collaborative filtering

9 years 2 months ago
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
Luis M. de Campos, Juan M. Fernández-Luna,
Added 28 May 2010
Updated 28 May 2010
Type Conference
Year 2009
Where RECSYS
Authors Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Miguel A. Rueda-Morales
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