A comparative evaluation of term weighting methods for information filtering

被引：6

作者：

Nanas, N ^{[1
]}

Uren, V ^{[1
]}

de Roeck, A ^{[1
]}

机构：

[1] Open Univ, Knowledge Media Inst, Milton Keynes MK7 6AA, Bucks, England

来源：

15TH INTERNATIONAL WORKSHOP ON DATABASE AND EXPERT SYSTEMS APPLICATIONS, PROCEEDINGS | 2004年

关键词：

D O I：

10.1109/DEXA.2004.1333442

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Users of information filtering systems can not be expected to provide large amounts of information to initialize a profile. Therefore, term weighting methods for information filtering have somewhat different requirements to those for information retrieval and text categorization. We present a comparative evaluation of term weighting methods, including a new method, relative document frequency, designed specifically for information filtering. The best weighting methods appear to be those that favor information provided by the user, over information from a general collection.

引用

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页码：13 / 17

页数：5

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