Group Recommender Systems: Aggregation, Satisfaction and Group Attributes
Published 1 January 2015
Judith Masthoff
Citations124
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TL;DR
This chapter shows how a system can recommend to a group of users by aggregating information from individual user models and modeling the user’s affective state and explores how group attributes can be incorporated in aggregation strategies.
Abstract
This group recommender system satisfaction chapter shows how a system can recommend to a group of users by aggregating information from individual user models and modeling the user's affective state affective state . It summarizes results from previous research in these areas. It explores how group attributes can be incorporated in aggregation aggregation strategies. Additionally, it shows how group recommendation group recommender system techniques can be applied when recommending to individuals, in particular for solving the cold-start problem and dealing with multiple criteria.
Keywords
Computer ScienceDecision SciencesBusiness, Management and Accounting
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