Explainable Entity-based Recommendations with Knowledge Graphs
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jul 2017.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 0; 2024: 0; 2025: 1.
5 papers.
- 2025CARTS: Collaborative Agents for Recommendation Textual Summarizationcited by 6
- 2021Explainable Student Performance Prediction With Personalized Attention for Explaining Why A Student Failscited by 2
- 2021Intent-based Product Collections for E-commerce using Pretrained Language Modelscited by 1
- 2022A Survey of Recommender System Techniques and the Ecommerce Domaincited by 1
- 2021Local Explanations for Clinical Search Engine resultscited by 0
- 2025CARTS: Collaborative Agents for Recommendation Textual Summarization21 Jun 2025
- 2022A Survey of Recommender System Techniques and the Ecommerce Domain15 Aug 2022
- 2021Local Explanations for Clinical Search Engine results19 Oct 2021
- 2021Explainable Student Performance Prediction With Personalized Attention for Explaining Why A Student Fails15 Oct 2021
- 2021Intent-based Product Collections for E-commerce using Pretrained Language Models15 Oct 2021