Preference Aggregation Mechanisms for a Tourism-Oriented Bayesian Recommender

In this work, we employ various preference aggregation mechanisms from the social choice literature alongside with a multiwinner voting rule, namely the Reweighted Approval Voting (RAV), to the group recommendations problem. In more detail, we equip with such mechanisms a Bayesian recommender system for the tourism domain, allowing for the effective aggregation of elicited group members’ preferences while promoting fairness in the group recommendations. We conduct a systematic experimental evaluation of our approach by applying it on a real-world dataset. Our results clearly demonstrate that the use of multiwinner mechanisms allows for fair group recommendations with respect to the well-known m-proportionality and m-envy-freeness metrics.