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Recommending twitter users to follow using content and collaborative filtering approaches
Author(s)
Date Issued
2010-09
Date Available
2010-10-19T15:23:58Z
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.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
ACM
Copyright (Published Version)
2010 ACM
Subject – LCSH
Web 2.0
Social media
Recommender systems (Information filtering)
Web versions
Language
English
Status of Item
Peer reviewed
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
This item is made available under a Creative Commons License
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