Difference between revisions of "Group recommendation"

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Examples are restaurant or movie recommendations for a party of several people (groups vary in composition),
 
Examples are restaurant or movie recommendations for a party of several people (groups vary in composition),
 
or TV program recommendation for a household (groups tend not to change much over time).
 
or TV program recommendation for a household (groups tend not to change much over time).
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One can distinguish between '''explicit group recommendation''', where recommendations are made for groups of known users, and '''implicit group recommendation''', where the group composition is not observed.
  
 
== See also ==
 
== See also ==
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== Literature ==
 
== Literature ==
* Mark O'Connor, Dan Cosley, [[Joseph A. Konstan]], [[John Riedl]]: ''PolyLens: A Recommender System for Groups of Users'', CSCW 2001
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* Mark O'Connor, Dan Cosley, [[Joseph A. Konstan]], [[John Riedl]]: ''[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.153.3077&rep=rep1&type=pdf PolyLens: A Recommender System for Groups of Users]'', [[CSCW]] 2001
* Linas Baltrunas, Tadas Makcinskas, Francesco Ricci: ''Group Recommendations with Rank Aggregation and Collaborative Filtering'', [[RecSys 2010]]
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* Christophe Senot, Dimitre Kostadinov, Makram Bouzid, Jérôme Picault, Armen Aghasaryan: ''[http://ijcai.org/papers11/Papers/IJCAI11-454.pdf Evaluation of Group Profiling Strategies]'', [[UMAP]] 2010
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* Linas Baltrunas, Tadas Makcinskas, [[Francesco Ricci]]: ''[http://www.inf.unibz.it/~ricci/papers/fp14-baltrunas.pdf Group Recommendations with Rank Aggregation and Collaborative Filtering]'', [[RecSys 2010]]
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[[Category:Task]]
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[[Category: Task]]

Latest revision as of 08:51, 21 July 2011

Group recommendation is the task of recommending items to groups of users instead of single users. Examples are restaurant or movie recommendations for a party of several people (groups vary in composition), or TV program recommendation for a household (groups tend not to change much over time).

One can distinguish between explicit group recommendation, where recommendations are made for groups of known users, and implicit group recommendation, where the group composition is not observed.

See also

Literature