MVIN: Learning Multiview Items for Recommendation

TL;DR

MVIN introduces a multi-view GNN approach for recommendation systems that leverages heterogeneous knowledge graphs from user and entity perspectives, significantly improving recommendation accuracy.

Contribution

The paper proposes MVIN, a novel GNN-based model that captures item properties from multiple views, addressing limitations of previous GNN approaches in recommendation systems.

Findings

MVIN outperforms state-of-the-art methods on three real-world datasets.

MVIN effectively captures user-attracting entities from user-view analysis.

Mixing layers in the GNN are crucial for aggregating neighborhood information.

Abstract

Researchers have begun to utilize heterogeneous knowledge graphs (KGs) as auxiliary information in recommendation systems to mitigate the cold start and sparsity issues. However, utilizing a graph neural network (GNN) to capture information in KG and further apply in RS is still problematic as it is unable to see each item's properties from multiple perspectives. To address these issues, we propose the multi-view item network (MVIN), a GNN-based recommendation model which provides superior recommendations by describing items from a unique mixed view from user and entity angles. MVIN learns item representations from both the user view and the entity view. From the user view, user-oriented modules score and aggregate features to make recommendations from a personalized perspective constructed according to KG entities which incorporates user click information. From the entity view, the mixing layer contrasts layer-wise GCN information to further obtain comprehensive features from internal entity-entity interactions in the KG. We evaluate MVIN on three real-world datasets: MovieLens-1M (ML-1M), LFM-1b 2015 (LFM-1b), and Amazon-Book (AZ-book). Results show that MVIN significantly outperforms state-of-the-art methods on these three datasets. In addition, from user-view cases, we find that MVIN indeed captures entities that attract users. Figures further illustrate that mixing layers in a heterogeneous KG plays a vital role in neighborhood information aggregation.