Using social ties in group recommendation

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Title: Using social ties in group recommendation
Authors: Bourke, Steven
McCarthy, Kevin
Smyth, Barry
Permanent link: http://hdl.handle.net/10197/3448
Date: 31-Aug-2011
Abstract: The social web is a mass of activity, petabytes of data are generated yearly. The social web has proven to be a great resource for new recommender system techniques and ideas. However it would appear that typically these techniques are not so social, as they only generate recommendations for a user acting alone. In this paper we take the social graph data and preference content (via Facebook) of 94 user study participants and generate social group recommendations for them and their friends. We evaluate how different aggregation policies perform in deciding the final group recommendation. Our findings show that in an offline evaluation an aggregation policy which takes into consideration social weighting outperforms other aggregation policies.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: Intelligent Systems Research Centre
Keywords: Recommender systemsCollaborative filteringGroup recommendation,AggregationSocial network
Subject LCSH: Recommender systems (Information filtering)
Web co-browsing
Online social networks
Language: en
Status of Item: Peer reviewed
Is part of: AICS 2011 : Proceedings of the 22nd Irish Conference on Artificial Intelligence and Cognitive Science : 31 August - 2 September, 2011 : University of Ulster - Magee
Conference Details: Paper presented at the 22nd Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2011), University of Ulster, Northern Ireland, 31 August - 2 September, 2011
Appears in Collections:CLARITY Research Collection
Computer Science Research Collection

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