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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Representation of Propositional Data for Collaborative Filtering
verfasst von : Andrzej Szwabe, Pawel Misiorek, Michal Ciesielczyk
Erschienen in: Distributed Computing and Artificial Intelligence
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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.
