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

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Abstract

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.

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