  Group recommender systems facilitate group decision making for a set of
individuals (e.g., a group of friends, a team, a corporation, etc.). Many of
these systems, however, either assume that (i) user preferences can be elicited
(or inferred) and then aggregated into group preferences or (ii) group
preferences are partially observed/elicited. 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: group decision prediction and reverse social choice. 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.
