SQUIRREL 2.0 : Fairness & Explanations for Sequential Group Recommendations

Abstract

A growing number of applications enable users to form groups for activities, like visiting a restaurant or watching a movie, making group recommenders more prevalent than ever. SQUIRREL is a framework for sequential group recommendations, providing a different recommendation in each round. It relies on Reinforcement Learning to select appropriate group recommendation algorithms based on the current state of the group. At each round of recommendations, it calculates the satisfaction of each group member and selects a recommendation method that will produce the maximum reward. In this paper, we incorporate two new reward functions, utilizing the m-proportionality measure to produce recommendations that are fairer to the group by promoting at least m items in the group recommendation list that each member prefers. Moreover, we study a user case explaining the SQUIRREL recommendations.