# Combining Embedding-Based and Semantic-Based Models for Post-hoc Explanations in Recommender Systems

arXiv: https://arxiv.org/abs/2401.04474 · HTML: https://mlsanity.com/paper/2401.04474/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 35 references that are in the map.
- First posted on arXiv 9 Jan 2024.

## Builds on (most cited first, 15 of 15)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [KGAT: Knowledge Graph Attention Network for Recommendation](https://mlsanity.com/paper/1905.07854/) (2019)
- [Explainable Recommendation: A Survey and New Perspectives](https://mlsanity.com/paper/1804.11192/) (2018)
- [A Survey on Knowledge Graph-Based Recommender Systems](https://mlsanity.com/paper/2003.00911/) (2020)
- [Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities](https://mlsanity.com/paper/2111.06420/) (2021)
- [Improving Question Answering by Commonsense-Based Pre-Training](https://mlsanity.com/paper/1809.03568/) (2018)
- [Collaborative Similarity Embedding for Recommender Systems](https://mlsanity.com/paper/1902.06188/) (2019)
- [Reinforced Path Reasoning for Counterfactual Explainable Recommendation](https://mlsanity.com/paper/2207.06674/) (2022)
- [Distilling Structured Knowledge into Embeddings for Explainable and Accurate Recommendation](https://mlsanity.com/paper/1912.08422/) (2019)
- [Improving Semantic Similarity Measure Within a Recommender System Based-on RDF Graphs](https://mlsanity.com/paper/2307.10639/) (2023)
- [Semantic Modeling for Food Recommendation Explanations](https://mlsanity.com/paper/2105.01269/) (2021)
- [Apport des ontologies pour le calcul de la similarit'e s'emantique au sein d'un syst`eme de recommandation](https://mlsanity.com/paper/2205.12539/) (2022)

## Built on by (most cited first, 4 of 4)

- [A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms](https://mlsanity.com/paper/2504.16420/) (2025)
- [A Survey of Controllable Learning: Methods and Applications in Information Retrieval](https://mlsanity.com/paper/2407.06083/) (2024)
- [Smart Multi-Modal Search: Contextual Sparse and Dense Embedding Integration in Adobe Express](https://mlsanity.com/paper/2408.14698/) (2024)
- [Enhancing Recommender Systems Using Textual Embeddings from Pre-trained Language Models](https://mlsanity.com/paper/2504.08746/) (2025)

## Abstract (arXiv)

In today's data-rich environment, recommender systems play a crucial role in decision support systems. They provide to users personalized recommendations and explanations about these recommendations. Embedding-based models, despite their widespread use, often suffer from a lack of interpretability, which can undermine trust and user engagement. This paper presents an approach that combines embedding-based and semantic-based models to generate post-hoc explanations in recommender systems, leveraging ontology-based knowledge graphs to improve interpretability and explainability. By organizing data within a structured framework, ontologies enable the modeling of intricate relationships between entities, which is essential for generating explanations. By combining embedding-based and semantic based models for post-hoc explanations in recommender systems, the framework we defined aims at producing meaningful and easy-to-understand explanations, enhancing user trust and satisfaction, and potentially promoting the adoption of recommender systems across the e-commerce sector.