Journal
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
Volume 7, Issue 1, Pages 80-89Publisher
ATLANTIS PRESS
DOI: 10.1080/18756891.2013.859861
Keywords
content-based filtering; recommender systems; weighting of features; entropy
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Funding
- Caja Rural de Jaen [TIN2012-31263, P08-TIC-03598, P10-AGR-6581, UJA2011/12/23]
- ERDF funds
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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.
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