# Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

arXiv: https://arxiv.org/abs/1805.03352 · HTML: https://mlsanity.com/paper/1805.03352/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2018.

## Built on by (most cited first, 30 of 39)

- [Learning Intents behind Interactions with Knowledge Graph for Recommendation](https://mlsanity.com/paper/2102.07057/) (2021)
- [Personalized Prompt Learning for Explainable Recommendation](https://mlsanity.com/paper/2202.07371/) (2022)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation](https://mlsanity.com/paper/2106.10879/) (2021)
- [Counterfactual Explanations for Neural Recommenders](https://mlsanity.com/paper/2105.05008/) (2021)
- [Generate Natural Language Explanations for Recommendation](https://mlsanity.com/paper/2101.03392/) (2021)
- [Vector-valued Distance and Gyrocalculus on the Space of Symmetric Positive Definite Matrices](https://mlsanity.com/paper/2110.13475/) (2021)
- [DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN](https://mlsanity.com/paper/2108.11883/) (2021)
- [Efficient Non-Sampling Knowledge Graph Embedding](https://mlsanity.com/paper/2104.10796/) (2021)
- [Causal Collaborative Filtering](https://mlsanity.com/paper/2102.01868/) (2021)
- [Knowledge-Enhanced Top-K Recommendation in Poincar'e Ball](https://mlsanity.com/paper/2101.04852/) (2021)
- [RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph](https://mlsanity.com/paper/2202.06129/) (2022)
- [Explanation as a Defense of Recommendation](https://mlsanity.com/paper/2101.09656/) (2021)
- [Comparative Explanations of Recommendations](https://mlsanity.com/paper/2111.00670/) (2021)
- [Comprehensible Artificial Intelligence on Knowledge Graphs: A survey](https://mlsanity.com/paper/2404.03499/) (2024)
- [Automatic Meta-Path Discovery for Effective Graph-Based Recommendation](https://mlsanity.com/paper/2112.12845/) (2021)
- [Entity Aware Negative Sampling with Auxiliary Loss of False Negative Prediction for Knowledge Graph Embedding](https://mlsanity.com/paper/2210.06242/) (2022)
- [Augmenting the User-Item Graph with Textual Similarity Models](https://mlsanity.com/paper/2109.09358/) (2021)
- [Counterfactual Collaborative Reasoning](https://mlsanity.com/paper/2307.00165/) (2023)
- [Finding Paths for Explainable MOOC Recommendation: A Learner Perspective](https://mlsanity.com/paper/2312.10082/) (2023)
- [Knowledge is Power, Understanding is Impact: Utility and Beyond Goals, Explanation Quality, and Fairness in Path Reasoning Recommendation](https://mlsanity.com/paper/2301.05944/) (2023)
- [Dual Policy Learning for Aggregation Optimization in Graph Neural Network-based Recommender Systems](https://mlsanity.com/paper/2302.10567/) (2023)
- [Neuro-Symbolic Recommendation Model based on Logic Query](https://mlsanity.com/paper/2309.07594/) (2023)
- [SOLARIS: Speculative Offloading of Latent-bAsed Representation for Inference Scaling](https://mlsanity.com/paper/2604.12110/) (2026)
- [Live Multi-Streaming and Donation Recommendations via Coupled Donation-Response Tensor Factorization](https://mlsanity.com/paper/2110.06117/) (2021)
- [Intent-based Product Collections for E-commerce using Pretrained Language Models](https://mlsanity.com/paper/2110.08241/) (2021)
- [InBox: Recommendation with Knowledge Graph using Interest Box Embedding](https://mlsanity.com/paper/2403.12649/) (2024)
- [KG4RecEval: Does Knowledge Graph Really Matter for Recommender Systems?](https://mlsanity.com/paper/2404.03164/) (2024)
- [Knowledge Graph Pruning for Recommendation](https://mlsanity.com/paper/2405.11531/) (2024)
- [Heterogeneous Hypergraph Embedding for Recommendation Systems](https://mlsanity.com/paper/2407.03665/) (2024)

## Abstract (arXiv)

Providing model-generated explanations in recommender systems is important to user experience. State-of-the-art recommendation algorithms - especially collaborative filtering (CF)-based approaches with shallow or deep models - usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or explicit feedbacks. Though structured knowledge bases were considered in content-based approaches, they have been largely ignored recently due to the research focus on CF approaches. However, structured knowledge exhibit unique advantages in personalized recommendation systems. When the explicit knowledge about users and items is considered for recommendation, the system could provide highly customized recommendations based on users' historical behaviors and the knowledge is helpful for providing informed explanations regarding the recommended items. A great challenge for using knowledge bases for recommendation is how to integrate large-scale structured data, while taking advantage of collaborative filtering for highly accurate performance. Recent achievements in knowledge-base embedding (KBE) sheds light on this problem, which makes it possible to learn user and item representations while preserving the structure of their relationship with external knowledge for explanation. In this work, we propose to explain knowledge-base embeddings for explainable recommendation. Specifically, we propose a knowledge-base representation learning framework to embed heterogeneous entities for recommendation, and based on the embedded knowledge base, a soft matching algorithm is proposed to generate personalized explanations for the recommended items. Experimental results on real-world e-commerce datasets verified the superior recommendation performance and the explainability power of our approach compared with state-of-the-art baselines.