Background
Group Recommender Systems (GRSys) are tools that help groups of people find items of interest and/or make decisions together [ 20 ]. GRSys employ algorithms and data to provide personalized recommendations to groups to satisfy the preferences (as much as possible) of all members of the group. These systems apply to a variety of domains and applications, including social media [31 , 32], online shopping [ 23], and collaborative work environments [ 8]. Known examples of GRSys include suggestions for a restaurant for a group of friends [16], or a project for a team to work on [12 ]. With research on single-users recommender systems (RecSys) growing over time [4], advancements in the area have been largely transferrable to GRSys. For example, methods such as collaborative filtering [ 30 ], constraint-based methods [ 13 ], rule-based [ 28], neural networks [6 ], and hybrid approaches [9] are applied to GRSys as much as other types of RecSys for single-users. However, specializing in groups often requires ad hoc approaches since their complex dynamics do not simply map to individuals’ needs and behaviors. Questions such as
“How to deal with different (or even opposing) preferences in the group? ”
“How to combine individual preferences?”
“Should the ratings of all group members have the same weight?”
are prevalent among GRSys and yet remain open-ended and hardly generalizable to different domains. More generally, ecological problems such as the generlizability, explainability, adaptation, privacy and fairness of the GRSys still present plenty of opportunities for research and development.
References
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