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

被引：24

作者：

Castro, Jorge ^{[1
]}

Rodriguez, Rosa M. ^{[1
]}

Barranco, Manuel J. ^{[1
]}

机构：

[1] Univ Jaen, Dept Comp Sci, Jaen 23071, Spain

来源：

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS | 2014年 / 7卷 / 01期

关键词：

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

D O I：

10.1080/18756891.2013.859861

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：80 / 89

页数：10

共 22 条

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