Abstract
In this paper a new proposal for memory-based Collaborative Filtering algorithms is presented. In order to compute its recommendations, a first step in memory-based methods is to find the neighborhood for the active user. Typically, this process is carried out by considering a vector-based similarity measure over the users’ ratings. This paper presents a new similarity criteria between users that could be used to both neighborhood selection and prediction processes. This criteria is based on the idea that if a user was good predicting the past ratings for the active user, then his/her predictions will be also valid in the future. Thus, instead of considering a vector-based measure between given ratings, this paper shows that it is possible to consider a distance between the real ratings (given by the active user in the past) and the ones predicted by a candidate neighbor. This distance measures the quality of each candidate neighbor at predicting the past ratings. The best-N predictors will be selected as the neighborhood.
This work has been jointly supported by the Spanish Ministerio de Ciencia e Innovación, under project TIN2008-06566-C04-01, and the Consejeria de Innovacion, Ciencia y Empresa de la Junta de Andalucia under project P09-TIC-4526.
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Cleger-Tamayo, S., Fernández-Luna, J.M., Huete, J.F. (2011). A New Criteria for Selecting Neighborhood in Memory-Based Recommender Systems. In: Lozano, J.A., Gámez, J.A., Moreno, J.A. (eds) Advances in Artificial Intelligence. CAEPIA 2011. Lecture Notes in Computer Science(), vol 7023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25274-7_43
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