Content-based recommender systems (CBRS) and collaborative filtering are the type of recommender systems most spread in the e-commerce arena. A CBRS works with two sets of information: (i) a set of features that describe the items to be recommended and (ii) a user's profile built from past choices that the user made over a subset of items. Based on these sets and on weighting items features the CBRS is able to recommend those items that better fits the user profile. Commonly, a CBRS deals with simple item features such as key words extracted from the item description applying a simple feature weighting model, based on the TF-IDF. However, this method does not obtain good results when features are assessed in multiple values and or domains. In this contribution we propose a higher level feature weighting method based on entropy and coefficients of correlation and contingency in order to improve the content-based filtering in settings with multi-valued features. © 2010 Springer-Verlag.
CITATION STYLE
Barranco, M. J., & Martínez, L. (2010). A method for weighting multi-valued features in content-based filtering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6098 LNAI, pp. 409–418). https://doi.org/10.1007/978-3-642-13033-5_42
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