Personalized News Recommendation Based on Collaborative Filtering

Because of the abundance of news on the web, news recommendation is an important problem. We compare three approaches for personalized news recommendation: collaborative filtering at the level of news items, content-based system recommending items with similar topics, and a hybrid technique. We obse...

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Vydáno v:2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Ročník 1; s. 437 - 441
Hlavní autoři: Garcin, Florent, Zhou, Kai, Faltings, Boi, Schickel, Vincent
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.12.2012
Témata:
Biological system modeling
Collaborative filtering
Filtering
History
Internet
news recommendation
Probabilistic logic
Recommender systems
Testing
Training
Vectors
ISBN:9781467360579, 1467360570
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Shrnutí:Because of the abundance of news on the web, news recommendation is an important problem. We compare three approaches for personalized news recommendation: collaborative filtering at the level of news items, content-based system recommending items with similar topics, and a hybrid technique. We observe that recommending items according to the topic profile of the current browsing session seems to give poor results. Although news articles change frequently and thus data about their popularity is sparse, collaborative filtering applied to individual articles provides the best results.
ISBN:9781467360579
1467360570
DOI:10.1109/WI-IAT.2012.95