Recommending twitter users to follow using content and collaborative filtering approaches

DSpace/Manakin Repository

Show simple item record

dc.contributor.author Hannon, John
dc.contributor.author Bennett, Mike
dc.contributor.author Smyth, Barry
dc.date.accessioned 2010-10-19T15:23:58Z
dc.date.available 2010-10-19T15:23:58Z
dc.date.copyright 2010 ACM en
dc.date.issued 2010-09
dc.identifier.uri http://hdl.handle.net/10197/2524
dc.description Paper presented at the 4th ACM Conference on Recommender Systems (RecSys 2010), Barcelona, Spain, September 26-30, 2010 en
dc.description.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. en
dc.description.sponsorship Science Foundation Ireland en
dc.format.extent 1276417 bytes
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher ACM en
dc.relation.ispartof RecSys'10 : proceedings of the 4th ACM Conference on Recommender Systems, Barcelona, Spain, September 26-30, 2010 en
dc.relation.requires CLARITY Research Collection en
dc.subject Web 2.0 en
dc.subject Twitter en
dc.subject Collaborative filtering en
dc.subject Content based recommendation en
dc.subject.lcsh Web 2.0 en
dc.subject.lcsh Social media en
dc.subject.lcsh Recommender systems (Information filtering) en
dc.title Recommending twitter users to follow using content and collaborative filtering approaches en
dc.type Conference Publication en
dc.internal.availability Full text available en
dc.internal.webversions Publisher's version en
dc.internal.webversions http://doi.acm.org/10.1145/1864708.1864746 en
dc.status Peer reviewed en
dc.identifier.doi 10.1145/1864708.1864746
dc.neeo.contributor Hannon|John|aut| en
dc.neeo.contributor Bennett|Mike|aut| en
dc.neeo.contributor Smyth|Barry|aut| en
dc.description.admin ti ke ab SB. 14/10/10. en


Files in this item

This item appears in the following Collection(s)

Show simple item record

This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.

If you are a publisher or author and have copyright concerns for any item, please email research.repository@ucd.ie and the item will be withdrawn immediately. The author or person responsible for depositing the article will be contacted within one business day.

Search Research Repository


Advanced Search

Browse