# Explainable Entity-based Recommendations with Knowledge Graphs

arXiv: https://arxiv.org/abs/1707.05254 · HTML: https://mlsanity.com/paper/1707.05254/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2017.

## Built on by (most cited first, 5 of 5)

- [CARTS: Collaborative Agents for Recommendation Textual Summarization](https://mlsanity.com/paper/2506.17765/) (2025)
- [Explainable Student Performance Prediction With Personalized Attention for Explaining Why A Student Fails](https://mlsanity.com/paper/2110.08268/) (2021)
- [Intent-based Product Collections for E-commerce using Pretrained Language Models](https://mlsanity.com/paper/2110.08241/) (2021)
- [A Survey of Recommender System Techniques and the Ecommerce Domain](https://mlsanity.com/paper/2208.07399/) (2022)
- [Local Explanations for Clinical Search Engine results](https://mlsanity.com/paper/2110.12891/) (2021)

## Abstract (arXiv)

Explainable recommendation is an important task. Many methods have been proposed which generate explanations from the content and reviews written for items. When review text is unavailable, generating explanations is still a hard problem. In this paper, we illustrate how explanations can be generated in such a scenario by leveraging external knowledge in the form of knowledge graphs. Our method jointly ranks items and knowledge graph entities using a Personalized PageRank procedure to produce recommendations together with their explanations.