Distilling Structured Knowledge into Embeddings for Explainable and Accurate Recommendation

TL;DR

This paper introduces a joint learning framework that distills structured knowledge from recommendation models to improve accuracy and explainability without additional computational costs.

Contribution

It presents a novel end-to-end method for embedding structured knowledge into recommendation models, enhancing both performance and interpretability.

Findings

Achieves state-of-the-art recommendation accuracy

Provides interpretable recommendation explanations

No extra overhead introduced in the process

Abstract

Recently, the embedding-based recommendation models (e.g., matrix factorization and deep models) have been prevalent in both academia and industry due to their effectiveness and flexibility. However, they also have such intrinsic limitations as lacking explainability and suffering from data sparsity. In this paper, we propose an end-to-end joint learning framework to get around these limitations without introducing any extra overhead by distilling structured knowledge from a differentiable path-based recommendation model. Through extensive experiments, we show that our proposed framework can achieve state-of-the-art recommendation performance and meanwhile provide interpretable recommendation reasons.