Probabilistic Majority Rule-Based Group Recommendation

Abstract

Group recommendation has received increased attention over the past decade. The fundamental challenge in group recommendation is how to aggregate the preferences of group members to select a set of items maximizing the overall satisfaction of the group. Different aggregation methods with different semantics have been proposed. In this paper, we explore a novel semantics of group recommendation, that is, probabilistic majority rule, allowing group members to make a "democratic" decision on which items are appropriate. Specifically, we propose a probabilistic model that captures the probability that a given item satisfies the majority of the group. We show that the naive strategy for computing such a probability is exponential time complexity, and propose an efficient dynamic programming approach to avoid this shortcoming. Furthermore, we design and develop an efficient algorithm, which leverages effective pruning techniques, for recommending the k items with the highest majority satisfaction probabilities. Finally, we demonstrate both the retrieval effectiveness and the efficiency of our approach through extensive experimental evaluation on real datasets.