Collaborative Interest-aware Graph Learning for Group Identification

TL;DR

This paper introduces CI4GI, a novel model that captures the dynamic, collaborative evolution of user interests at both group and item levels to improve group recommendation accuracy on social media platforms.

Contribution

The paper proposes a new interest enhancement strategy and a negative sampling method to better model the dual-level interest evolution in group identification tasks.

Findings

CI4GI outperforms existing models on three real-world datasets.

Interest enhancement improves the modeling of user interests.

Optimized negative sampling reduces false-negative impact.

Abstract

With the popularity of social media, an increasing number of users are joining group activities on online social platforms. This elicits the requirement of group identification (GI), which is to recommend groups to users. We reveal that users are influenced by both group-level and item-level interests, and these dual-level interests have a collaborative evolution relationship: joining a group expands the user's item interests, further prompting the user to join new groups. Ultimately, the two interests tend to align dynamically. However, existing GI methods fail to fully model this collaborative evolution relationship, ignoring the enhancement of group-level interests on item-level interests, and suffering from false-negative samples when aligning cross-level interests. In order to fully model the collaborative evolution relationship between dual-level user interests, we propose CI4GI, a Collaborative Interest-aware model for Group Identification. Specifically, we design an interest enhancement strategy that identifies additional interests of users from the items interacted with by the groups they have joined as a supplement to item-level interests. In addition, we adopt the distance between interest distributions of two users to optimize the identification of negative samples for a user, mitigating the interference of false-negative samples during cross-level interests alignment. The results of experiments on three real-world datasets demonstrate that CI4GI significantly outperforms state-of-the-art models.