DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN

TL;DR

This paper introduces DSKReG, a novel differentiable sampling method on knowledge graphs that improves recommendation accuracy by learning relevance distributions and jointly optimizing sampling with model training.

Contribution

The paper proposes a differentiable sampling strategy for knowledge graphs in recommender systems, addressing skewed node degrees and irrelevant interactions, which is a novel approach.

Findings

Outperforms state-of-the-art KG-based recommenders

Effectively learns relevance distributions for sampling

Joint optimization improves recommendation quality

Abstract

In the information explosion era, recommender systems (RSs) are widely studied and applied to discover user-preferred information. A RS performs poorly when suffering from the cold-start issue, which can be alleviated if incorporating Knowledge Graphs (KGs) as side information. However, most existing works neglect the facts that node degrees in KGs are skewed and massive amount of interactions in KGs are recommendation-irrelevant. To address these problems, in this paper, we propose Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN (DSKReG) that learns the relevance distribution of connected items from KGs and samples suitable items for recommendation following this distribution. We devise a differentiable sampling strategy, which enables the selection of relevant items to be jointly optimized with the model training procedure. The experimental results demonstrate that our model outperforms state-of-the-art KG-based recommender systems. The code is available online at https://github.com/YuWang-1024/DSKReG.