Personalized News Recommendation Based on Collaborative Filtering

Authors:
Florent Garcin;Kai Zhou;Boi Faltings;Vincent Schickel
Affiliations:
-;-;-;-
Venue:
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Year:
2012

Citing 13
Cited 1

GroupLens: an open architecture for collaborative filtering of netnews

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GroupLens: applying collaborative filtering to Usenet news

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Probabilistic latent semantic indexing

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A hybrid user model for news story classification

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Hybrid Recommender Systems: Survey and Experiments

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Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions

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An MDP-Based Recommender System

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Open user profiles for adaptive news systems: help or harm?

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Google news personalization: scalable online collaborative filtering

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Personalized news recommendation based on click behavior

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A survey of collaborative filtering techniques

Advances in Artificial Intelligence
A contextual-bandit approach to personalized news article recommendation

Proceedings of the 19th international conference on World wide web

PEN recsys: a personalized news recommender systems framework

Proceedings of the 2013 International News Recommender Systems Workshop and Challenge

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Abstract

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.