4.7 Article

A novel KG-based recommendation model via relation-aware attentional GCN

Journal

KNOWLEDGE-BASED SYSTEMS
Volume 275, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.knosys.2023.110702

Keywords

Recommender system; Graph convolutional networks; Knowledge graph; User preference; Item attractiveness

Ask authors/readers for more resources

Leveraging knowledge graphs (KGs) via graph convolutional networks (GCNs) to enhance recommender systems has gained considerable attention. However, existing approaches fail to consider entity pairs without relations, which may possess important information. To address this, we propose a novel relation-aware attentional GCN (RAAGCN) that aggregates entity pairs with and without explicit relations and distinguishes the importance of relational context information. Based on RAAGCN, we propose a user preference and item attractiveness capturing model (UPIACM) that decomposes user preference into interest and rating preferences and incorporates item attractiveness. Our model outperforms state-of-the-art baseline methods.
Leveraging knowledge graphs (KGs) to enhance recommender systems has gained considerable atten-tion, with researchers obtaining user preferences by aggregating entity pairs with explicit relations in KGs via graph convolutional networks (GCNs). Existing approaches currently overlook many entity pairs without relations, which, however, may have potentially useful information. To address this issue, we propose a novel relation-aware attentional GCN (RAAGCN) with the following improvements over vanilla GCNs: (1) it aggregates all entity pairs with and without explicit relations and (2) it distinguishes the importance of different relational context information. Based on the proposed RAAGCN, we further propose a user preference and item attractiveness capturing model (UPIACM) for KG-based recommendation. In the UPIACM, the user preference is decomposed into interest and rating preferences. The interest preference is the user's interest taste toward the items with specific features, while the rating preference reflects the intention of rating high or low. Additionally, our model accounts for item attractiveness, which reflects an item's popularity among users. Additionally, we incorporate a gated filtering mechanism to further improve our model's performance. Through extensive experiments, we show that the proposed UPIACM outperforms state-of-the-art baseline methods. & COPY; 2023 Elsevier B.V. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available