Dependency Relationships-Enhanced Attentive Group Recommendation in HINs

TL;DR

This paper introduces DREAGR, a novel group recommendation model that leverages dependency relationships between items and path-aware attention to improve recommendation accuracy for groups, especially under data sparsity.

Contribution

The paper proposes a new method that incorporates item dependency relationships and path-aware attention to enhance group recommendation performance in HINs.

Findings

DREAGR outperforms state-of-the-art models in HR@N and NDCG@N metrics.

Dependency relationships help alleviate interaction sparsity.

Path-aware attention improves user preference modeling.

Abstract

Recommending suitable items to a group of users, commonly referred to as the group recommendation task, is becoming increasingly urgent with the development of group activities. The challenges within the group recommendation task involve aggregating the individual preferences of group members as the group's preferences and facing serious sparsity problems due to the lack of user/group-item interactions. To solve these problems, we propose a novel approach called Dependency Relationships-Enhanced Attentive Group Recommendation (DREAGR) for the recommendation task of occasional groups. Specifically, we introduce the dependency relationship between items as side information to enhance the user/group-item interaction and alleviate the interaction sparsity problem. Then, we propose a Path-Aware Attention Embedding (PAAE) method to model users' preferences on different types of paths. Next, we design a gated fusion mechanism to fuse users' preferences into their comprehensive preferences. Finally, we develop an attention aggregator that aggregates users' preferences as the group's preferences for the group recommendation task. We conducted experiments on two datasets to demonstrate the superiority of DREAGR by comparing it with state-of-the-art group recommender models. The experimental results show that DREAGR outperforms other models, especially HR@N and NDCG@N (N=5, 10), where DREAGR has improved in the range of 3.64% to 7.01% and 2.57% to 3.39% on both datasets, respectively.