# How to make latent factors interpretable by feeding Factorization machines with knowledge graphs

arXiv: https://arxiv.org/abs/1909.05038 · HTML: https://mlsanity.com/paper/1909.05038/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2019.

## Built on by (most cited first, 7 of 7)

- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [Recommender systems based on graph embedding techniques: A comprehensive review](https://mlsanity.com/paper/2109.09587/) (2021)
- [Comprehensible Artificial Intelligence on Knowledge Graphs: A survey](https://mlsanity.com/paper/2404.03499/) (2024)
- [Reenvisioning Collaborative Filtering vs Matrix Factorization](https://mlsanity.com/paper/2107.13472/) (2021)
- [A review of feature selection strategies utilizing graph data structures and knowledge graphs](https://mlsanity.com/paper/2406.14864/) (2024)
- [Conversational Recommendation: Theoretical Model and Complexity Analysis](https://mlsanity.com/paper/2111.05578/) (2021)
- [WARBERT: A Hierarchical BERT-based Model for Web API Recommendation](https://mlsanity.com/paper/2509.23175/) (2025)

## Abstract (arXiv)

Model-based approaches to recommendation can recommend items with a very high level of accuracy. Unfortunately, even when the model embeds content-based information, if we move to a latent space we miss references to the actual semantics of recommended items. Consequently, this makes non-trivial the interpretation of a recommendation process. In this paper, we show how to initialize latent factors in Factorization Machines by using semantic features coming from a knowledge graph in order to train an interpretable model. With our model, semantic features are injected into the learning process to retain the original informativeness of the items available in the dataset. The accuracy and effectiveness of the trained model have been tested using two well-known recommender systems datasets. By relying on the information encoded in the original knowledge graph, we have also evaluated the semantic accuracy and robustness for the knowledge-aware interpretability of the final model.