Group Recommender Systems: Aggregation, Satisfaction and Group Attributes

2015 ◽  
pp. 743-776 ◽  
Author(s):  
Judith Masthoff
Keyword(s):  
Recommender Systems ◽  
Group Recommender Systems ◽  
Group Attributes ◽  
Group Recommender

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.

Virtual user approach for group recommender systems using precedence relations

2015 ◽  
Vol 294 ◽  
pp. 15-30 ◽  
Author(s):  
Venkateswara Rao Kagita ◽  
Arun K. Pujari ◽  
Vineet Padmanabhan
Keyword(s):  
Recommender Systems ◽  
Group Recommender Systems ◽  
Precedence Relations ◽  
Group Recommender

Can Automated Group Recommender Systems Help Consumers Make Better Choices?

2012 ◽  
Vol 76 (5) ◽  
pp. 89-109 ◽  
Author(s):  
Thorsten Hennig-Thurau ◽  
André Marchand ◽  
Paul Marx
Keyword(s):  
Recommender Systems ◽  
Group Recommender Systems ◽  
Group Recommender

scholarly journals Hierarchy Visualization for Group Recommender Systems

2019 ◽  
Vol 49 (6) ◽  
pp. 1152-1163 ◽  
Author(s):  
Wei Wang ◽  
Guangquan Zhang ◽  
Jie Lu
Keyword(s):  
Recommender Systems ◽  
Group Recommender Systems ◽  
Group Recommender

scholarly journals Dynamic Group Recommendation Algorithm Based on Member Activity Level

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Junjie Jia ◽  
Yewang Yao ◽  
Zhipeng Lei ◽  
Pengtao Liu
Keyword(s):  
Recommender Systems ◽  
Activity Level ◽  
Group Recommendation ◽  
Recommendation Algorithm ◽  
Group Recommender Systems ◽  
Dynamic Group ◽  
Recent Activity ◽  
Group Profile ◽  
The Impact ◽  
Group Recommender

The rapid development of social networks has led to an increased desire for group entertainment consumption, making the study of group recommender systems a hotspot. Existing group recommender systems focus too much on member preferences and ignore the impact of member activity level on recommendation results. To this end, a dynamic group recommendation algorithm based on the activity level of members is proposed. Firstly, the algorithm predicts the unknown preferences of members using a time-series-oriented rating prediction model. Secondly, considering the dynamic change of member activity level, the group profile is generated by designing a sliding time window to investigate the recent activity level of each member in the group at the recommended moment, and preference is aggregated based on the recent activity level of members. Finally, the group recommendations are generated based on the group profile. The experimental results show that the algorithm in this paper achieves a better recommendation result.

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