News recommendation in Indonesian language based on user click behavior

Author

Desyaputri, Diandra Mayang ; Erwin, Alva ; Galinium, Maulahikmah ; Nugrahadi, Didi

Author_Institution

Dept. of Inf. Technol., Swiss German Univ., Tangerang, Indonesia

fYear

2013

fDate

7-8 Oct. 2013

Firstpage

164

Lastpage

169

Abstract

Recommendation system has been proposed for years as the solution of information era problem. This research strives to develop an intelligent recommendation system based on user click behavior on news websites. We extracted frequent itemsets and association rules from the web server log of a news website, performed a pre-computation of similarity between news articles, and then proposed a three-level recommendation system: based on association rule discovery, news articles on the same category, and similarity between news articles. By combining collaborative filtering approach and content-based filtering, experiment results show that the technique produces reliable news recommendation.

Keywords

collaborative filtering; data mining; recommender systems; Indonesian language; association rule discovery; association rules extraction; collaborative filtering approach; content-based filtering; frequent itemsets extraction; information era problem; intelligent recommendation system; news recommendation system; three-level recommendation system; user click behavior; association rules; news recommendation; similarity; web usage mining;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Technology and Electrical Engineering (ICITEE), 2013 International Conference on

Conference_Location

Yogyakarta

Print_ISBN

978-1-4799-0423-5

Type

conf

DOI

10.1109/ICITEED.2013.6676232

Filename

6676232