Group representation learning for group recommendation

Abstract

Group recommender systems facilitate group decision making for a set of individuals (e.g., a group of friends, a team, a corporation, etc.). Existing group recommendation methods mostly learn group members' individual preferences and then aggregate them into a group preference. This thesis takes a different approach. We focus on making recommendations for a new group of users whose preferences are unknown, but we are given the decisions/choices of other groups. By formulating this problem as group recommendation from group implicit feedback, we focus on two of its practical instances: Given a set of groups and their observed decisions, group decision prediction intends to predict the decision of a new group of users whereas reverse social choice aims to infer the preferences of those users involved in observed group decisions. These two problems are of interest to not only group recommendation, but also to personal privacy when the users intend to conceal their personal preferences, but have participated in group decisions. To tackle these two problems, we propose and study DeepGroup – a deep learning approach for group recommendation with group implicit data. We empirically assess the predictive power of DeepGroup on various real-world datasets, group conditions (e.g., homophily or heterophily), and group decision (or voting) rules. Our extensive experiments not only demonstrate the efficacy of DeepGroup but also shed light on the privacy-leakage concerns of some decision making processes.