Recommending twitter users to follow using content and collaborative filtering approaches

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Title: Recommending twitter users to follow using content and collaborative filtering approaches
Authors: Hannon, John
Bennett, Mike
Smyth, Barry
Permanent link: http://hdl.handle.net/10197/2524
Date: Sep-2010
Abstract: 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.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: ACM
Copyright (published version): 2010 ACM
Keywords: Web 2.0TwitterCollaborative filteringContent based recommendation
Subject LCSH: Web 2.0
Social media
Recommender systems (Information filtering)
DOI: 10.1145/1864708.1864746
Language: en
Status of Item: Peer reviewed
Is part of: RecSys'10 : proceedings of the 4th ACM Conference on Recommender Systems, Barcelona, Spain, September 26-30, 2010
Conference Details: Paper presented at the 4th ACM Conference on Recommender Systems (RecSys 2010), Barcelona, Spain, September 26-30, 2010
Appears in Collections:CLARITY Research Collection
Computer Science Research Collection

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