# Distilling Structured Knowledge into Embeddings for Explainable and Accurate Recommendation

arXiv: https://arxiv.org/abs/1912.08422 · HTML: https://mlsanity.com/paper/1912.08422/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2019.

## Built on by (most cited first, 10 of 10)

- [LLAVADI: What Matters For Multimodal Large Language Models Distillation](https://mlsanity.com/paper/2407.19409/) (2024)
- [Unbiased Knowledge Distillation for Recommendation](https://mlsanity.com/paper/2211.14729/) (2022)
- [Privileged Graph Distillation for Cold Start Recommendation](https://mlsanity.com/paper/2105.14975/) (2021)
- [Comprehensible Artificial Intelligence on Knowledge Graphs: A survey](https://mlsanity.com/paper/2404.03499/) (2024)
- [Toward Student-Oriented Teacher Network Training For Knowledge Distillation](https://mlsanity.com/paper/2206.06661/) (2022)
- [Active Large Language Model-based Knowledge Distillation for Session-based Recommendation](https://mlsanity.com/paper/2502.15685/) (2024)
- [Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering](https://mlsanity.com/paper/2202.13140/) (2022)
- [Combining Embedding-Based and Semantic-Based Models for Post-hoc Explanations in Recommender Systems](https://mlsanity.com/paper/2401.04474/) (2024)
- [From Consensus to Disagreement: Multi-Teacher Distillation for Semi-Supervised Relation Extraction](https://mlsanity.com/paper/2112.01048/) (2021)
- [Infrared Spectra Prediction for Diazo Groups Utilizing a Machine Learning Approach with Structural Attention Mechanism](https://mlsanity.com/paper/2402.03112/) (2024)

## Abstract (arXiv)

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.