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

TL;DR

This paper proposes a method to initialize latent factors in Factorization Machines using knowledge graph-derived semantic features, enhancing interpretability without sacrificing accuracy in recommendation systems.

Contribution

It introduces a novel approach to incorporate knowledge graph semantics into Factorization Machines for improved interpretability and maintains competitive recommendation accuracy.

Findings

Semantic features improve interpretability of recommendations.

Model retains high accuracy on benchmark datasets.

Knowledge graph information enhances robustness and semantic correctness.

Abstract

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.