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Probabilistic group recommendation via information matching

Gorla, J; Lathia, N; Robertson, S; Wang, J; (2013) Probabilistic group recommendation via information matching. In: WWW 2013 Proceedings. (pp. pp. 495-504). ACM Green open access

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Increasingly, web recommender systems face scenarios where they need to serve suggestions to groups of users; for exam- ple, when families share e-commerce or movie rental web accounts. Research to date in this domain has proposed two approaches: computing recommendations for the group by merging any members’ ratings into a single profile, or com- puting ranked recommendations for each individual that are then merged via a range of heuristics. In doing so, none of the past approaches reason on the preferences that arise in individuals when they are members of a group . In this work, we present a probabilistic framework, based on the notion of information matching, for group recommendation. This model defines group relevance as a combination of the item’s relevance to each user as an individual and as a member of the group; it can then seamlessly incorporate any group rec- ommendation strategy in order to rank items for a set of individuals. We evaluate the model’s efficacy at generating recommendations for both single individuals and groups us- ing the MovieLens and MoviePilot data sets. In both cases, we compare our results with baselines and state-of-the-art collaborative filtering algorithms, and show that the model outperforms all others over a variety of ranking metrics.

Type: Proceedings paper
Title: Probabilistic group recommendation via information matching
Event: IW3C2 WWW 2013 Conference, May 13–17, 2013, Rio de Janeiro, Brazil
ISBN-13: 9781450320351
Open access status: An open access version is available from UCL Discovery
Publisher version: http://www2013.org/proceedings/p495.pdf
Language: English
Additional information: © World Wide Web Conference Committee (IW3C2) 2013. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in http://www2013.org/papers/., http://www2013.org/proceedings/p495.pdf
Keywords: Probabilistic modelling, Group recommendation
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1385632
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