1995 NewsWeederLearntoFiltNetnews
- (Lang, 1995) ⇒ Ken Lang. (1995). “NewsWeeder: Learning to Filter Netnews.” In: Proceedings of the 12th International Machine Learning Conference (ICML 1995).
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Abstract
A significant problem in many information filtering systems is the dependence on the user for the creation and maintenance of a user profile, which describes the user's interests. NewsWeeder is a netnews-filtering system that addresses this problem by letting the user rate his or her interest level for each article being read (1-5), and then learning a user profile based on these ratings. This paper describes how NewsWeeder accomplishes this task, and examines the alternative learning methods used. The results show that a learning algorithm based on the Minimum Description Length (MDL) principle was able to raise the percentage of interesting articles to be shown to users from 14% to 52% on average. Further, this performance significantly outperformed (by 21%) one of the most successful techniques in Information Retrieval (IR), termfrequency /inverse-document-frequency (tf-idf) weighting.
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1995 NewsWeederLearntoFiltNetnews | Ken Lang | NewsWeeder: Learning to Filter Netnews | http://www.citeulike.org/group/4225/article/1607788 |