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2010
ACM

Recommending twitter users to follow using content and collaborative filtering approaches

9 years 2 months ago
Recommending twitter users to follow using content and collaborative filtering approaches
Recently the world of the web has become more social and more real-time. Facebook and Twitter are perhaps the exemplars of a new generation of social, real-time web services and we believe these types of service provide a fertile ground for recommender systems research. In this paper we focus on one of the key features of the social web, namely the creation of relationships between users. Like recent research, we view this as an important recommendation problem -- for a given user, UT which other users might be recommended as followers/followees -- but unlike other researchers we attempt to harness the real-time web as the basis for profiling and recommendation. To this end we evaluate a range of different profiling and recommendation strategies, based on a large dataset of Twitter users and their tweets, to demonstrate the potential for effective and efficient followee recommendation. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: Information filtering; ...
John Hannon, Mike Bennett, Barry Smyth
Added 06 Dec 2010
Updated 06 Dec 2010
Type Conference
Year 2010
Where RECSYS
Authors John Hannon, Mike Bennett, Barry Smyth
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