?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=Probabilistic+group+recommendation+via+information+matching&rft.creator=Gorla%2C+J&rft.creator=Lathia%2C+N&rft.creator=Robertson%2C+S&rft.creator=Wang%2C+J&rft.description=Increasingly%2C+web+recommender+systems+face+scenarios+where+they+need+to+serve+suggestions+to+groups+of+users%3B+for+exam-+ple%2C+when+families+share+e-commerce+or+movie+rental+web+accounts.+Research+to+date+in+this+domain+has+proposed+two+approaches%3A+computing+recommendations+for+the+group+by+merging+any+members%E2%80%99+ratings+into+a+single+profile%2C+or+com-+puting+ranked+recommendations+for+each+individual+that+are+then+merged+via+a+range+of+heuristics.+In+doing+so%2C+none+of+the+past+approaches+reason+on+the+preferences+that+arise+in+individuals+when+they+are+members+of+a+group+.+In+this+work%2C+we+present+a+probabilistic+framework%2C+based+on+the+notion+of+information+matching%2C+for+group+recommendation.+This+model+defines+group+relevance+as+a+combination+of+the+item%E2%80%99s+relevance+to+each+user+as+an+individual+and+as+a+member+of+the+group%3B+it+can+then+seamlessly+incorporate+any+group+rec-+ommendation+strategy+in+order+to+rank+items+for+a+set+of+individuals.+We+evaluate+the+model%E2%80%99s+efficacy+at+generating+recommendations+for+both+single+individuals+and+groups+us-+ing+the+MovieLens+and+MoviePilot+data+sets.+In+both+cases%2C+we+compare+our+results+with+baselines+and+state-of-the-art+collaborative+filtering+algorithms%2C+and+show+that+the+model+outperforms+all+others+over+a+variety+of+ranking+metrics.&rft.subject=Probabilistic+modelling%2C+Group+recommendation&rft.publisher=ACM&rft.date=2013&rft.type=Proceedings+paper&rft.language=eng&rft.source=+++++In%3A++WWW+2013+Proceedings.++(pp.+pp.+495-504).++ACM+(2013)+++++&rft.format=application%2Fpdf&rft.identifier=https%3A%2F%2Fdiscovery.ucl.ac.uk%2Fid%2Feprint%2F1385632%2F1%2Fwww2013-2.pdf&rft.identifier=https%3A%2F%2Fdiscovery.ucl.ac.uk%2Fid%2Feprint%2F1385632%2F&rft.rights=open