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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Main Authors: Garcin, F., Zhou, K., Faltings, B., Schickel, V.
Format: Conference Proceeding
Language:English
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collaborative filtering
news recommendation
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Zhou, K.
Faltings, B.
Schickel, V.
description 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.
doi_str_mv 10.1109/WI-IAT.2012.95
format conference_proceeding
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subjects collaborative filtering
news recommendation
title Personalized News Recommendation Based on Collaborative Filtering
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