AlignGroup: Learning and Aligning Group Consensus with Member Preferences for Group Recommendation

TL;DR

AlignGroup introduces a novel group recommendation approach that simultaneously models group consensus and individual preferences using hypergraph neural networks and self-supervised alignment, achieving superior performance on real-world datasets.

Contribution

The paper presents AlignGroup, a new method combining hypergraph neural networks and self-supervised alignment to better capture group consensus and individual preferences in recommendations.

Findings

Outperforms state-of-the-art methods on two real-world datasets.

Effectively captures intra- and inter-group relationships.

Improves recommendation accuracy and efficiency.

Abstract

Group activities are important behaviors in human society, providing personalized recommendations for groups is referred to as the group recommendation task. Existing methods can usually be categorized into two strategies to infer group preferences: 1) determining group preferences by aggregating members' personalized preferences, and 2) inferring group consensus by capturing group members' coherent decisions after common compromises. However, the former would suffer from the lack of group-level considerations, and the latter overlooks the fine-grained preferences of individual users. To this end, we propose a novel group recommendation method AlignGroup, which focuses on both group consensus and individual preferences of group members to infer the group decision-making. Specifically, AlignGroup explores group consensus through a well-designed hypergraph neural network that efficiently learns intra- and inter-group relationships. Moreover, AlignGroup innovatively utilizes a self-supervised alignment task to capture fine-grained group decision-making by aligning the group consensus with members' common preferences. Extensive experiments on two real-world datasets validate that our AlignGroup outperforms the state-of-the-art on both the group recommendation task and the user recommendation task, as well as outperforms the efficiency of most baselines.