GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation

TL;DR

This paper introduces GroupIM, a neural recommendation framework that enhances ephemeral group recommendations by maximizing mutual information and prioritizing informative members, significantly improving performance over existing methods.

Contribution

The paper proposes a novel, architecture-agnostic framework that regularizes user-group representations using mutual information maximization and contextual weighting to address interaction sparsity.

Findings

Achieves 31-62% relative NDCG@20 improvement

Effectively models preference covariance among group members

Demonstrates robustness across multiple real-world datasets

Abstract

We study the problem of making item recommendations to ephemeral groups, which comprise users with limited or no historical activities together. Existing studies target persistent groups with substantial activity history, while ephemeral groups lack historical interactions. To overcome group interaction sparsity, we propose data-driven regularization strategies to exploit both the preference covariance amongst users who are in the same group, as well as the contextual relevance of users' individual preferences to each group. We make two contributions. First, we present a recommender architecture-agnostic framework GroupIM that can integrate arbitrary neural preference encoders and aggregators for ephemeral group recommendation. Second, we regularize the user-group latent space to overcome group interaction sparsity by: maximizing mutual information between representations of groups and group members; and dynamically prioritizing the preferences of highly informative members through contextual preference weighting. Our experimental results on several real-world datasets indicate significant performance improvements (31-62% relative NDCG@20) over state-of-the-art group recommendation techniques.