Group Recommender Systems: New Perspectives in the Social Web

2012 ◽  
pp. 139-157 ◽  
Author(s):  
Iván Cantador ◽  
Pablo Castells
Keyword(s):  
Recommender Systems ◽  
Social Web ◽  
Group Recommender Systems ◽  
The Social ◽  
Group Recommender

Workshop on recommender systems and the social web

2009 ◽  
Author(s):  
Dietmar Jannach ◽  
Werner Geyer ◽  
Casey Dugan ◽  
Jill Freyne ◽  
Sarabjot Singh Anand ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Social Web ◽  
The Social

Group Recommender Systems – An Evolutionary Approach Based on Multi-expert System for Consensus

2018 ◽  
Vol 29 (1) ◽  
pp. 1092-1108 ◽  
Author(s):  
Ritu Meena ◽  
Sonajharia Minz
Keyword(s):  
Recommender Systems ◽  
Group Recommendation ◽  
Group Recommender Systems ◽  
Maximum Utility ◽  
Group Members ◽  
Two Phases ◽  
The Individual ◽  
Multiple Experts ◽  
Group Profile ◽  
Group Recommender

Abstract Recommender systems have focused on algorithms for a recommendation for individuals. However, in many domains, it may be recommending an item, for example, movies, restaurants etc. for a group of persons for which some remarkable group recommender systems (GRSs) has been developed. GRSs satisfy a group of people optimally by considering the equal weighting of the individual preferences. We have proposed a multi-expert scheme (MES) for group recommendation using genetic algorithm (GA) MES-GRS-GA that depends on consensus techniques to further improve group recommendations. In order to deal with this problem of GRS, we also propose a consensus scheme for GRSs where consensus from multiple experts are brought together to make a single recommended list of items in which each expert represents an individual inside the group. The proposed GA based consensus scheme is modeled as many consensus schemes within two phases. In the consensus phase, we have applied GA to obtain the maximum utility offer for each expert and generated the most appropriate rating for each item in the group. In the recommendation generation phase, again GA has been employed to produce the resulting group profile, i.e. the list of ratings with the minimum sum of distances from the group members. Finally, the results of computational experiments that bear close resemblance to real-world scenarios are presented and compared to baseline GRS techniques that illustrate the superiority of the proposed model.

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