Abstract
State-of-the-art approaches to collaborative filtering are based on the use of an input matrix that represents each user profile as a vector in a space of items and, analogically, each item as a vector in a space of users. When the behavioral input data have the form of (userX, likes, itemY) and (userX, dislikes, itemY) triples, one has to propose a bi-relational data representation that is more flexible than the ordinary user-item ratings matrix. We propose to use a matrix, in which columns represent RDF-like triples and rows represent users, items, and relations. We show that the proposed behavioral data representation based on the use of an element-fact matrix, combined with reflective matrix processing, enables outperforming state-of-the- art collaborative filtering methods based on the use of a ’standard’ user-item matrix.
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Szwabe, A., Misiorek, P., Ciesielczyk, M. (2013). Representation of Propositional Data for Collaborative Filtering. In: Omatu, S., Neves, J., Rodriguez, J., Paz Santana, J., Gonzalez, S. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 217. Springer, Cham. https://doi.org/10.1007/978-3-319-00551-5_47
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DOI: https://doi.org/10.1007/978-3-319-00551-5_47
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-00550-8
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