Weighting of Features in Content-Based Filtering with Entropy and Dependence Measures

DOI

10.1080/18756891.2013.859861How to use a DOI?

Keywords

content-based filtering, recommender systems, weighting of features, entropy

Abstract

Content-based recommender systems (CBRS) are tools that help users to choose items when they face a huge amount of options, recommending items that better fit the user's profile. In such a process, it is very interesting to know which features of the items are more important for each user, thus the CBRS provides them higher weight. The Term Frequency-Inverse Document Frequency (TF-IDF) method is one of the most used for weighting of features, however, it does not provide the best results when the features are multi-valued. In this contribution, it is proposed a new method for obtaining the weights of the features by means of entropy and coefficients of dependency.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Jorge Castro
AU  - Rosa M. Rodriguez
AU  - Manuel J. Barranco
PY  - 2014
DA  - 2014/02/03
TI  - Weighting of Features in Content-Based Filtering with Entropy and Dependence Measures
JO  - International Journal of Computational Intelligence Systems
SP  - 80
EP  - 89
VL  - 7
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.859861
DO  - 10.1080/18756891.2013.859861
ID  - Castro2014
ER  -