Modeling Uncertainty in Group Recommendations

Abstract

In many settings, it is required that items are recommended to a group of users instead of a single user. Most often, when the decision criteria and preferences of the group as a whole are not known, the gold standard is to aggregate individual member preferences or recommendations. Such techniques typically presuppose some process under which group members reach consensus, e.g., least misery, maximum satisfaction, disregarding any uncertainty on whether this presumption is accurate. We propose a different approach that explicitly models the system's uncertainty in the way members might agree on a group ranking. The basic idea is to quantify the likelihood of hypothetical group rankings based on the observed member's individual rankings. Then, the systems recommends a ranking that has the highest expected reward with respect to the hypothetical rankings. Experiments with real and synthetic groups demonstrate the superiority of this approach compared to previous work based on aggregation strategies and to recent fairness-aware techniques.