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

18th International Semantic Web Conference (ISWC 2019) - 2019
Download the publication : 2019_Anelli_ISWC2019.pdf [623Ko]  
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.

BibTex references


@InProceedings{ADDRT19c,
author = {Vito Walter Anelli and Tommaso {Di Noia} and Eugenio {Di Sciascio} and Azzurra Ragone and Joseph Trotta},
title = "How to make latent factors interpretable by
feeding Factorization machines with knowledge
graphs",
booktitle = "18th International Semantic Web Conference (ISWC
2019)",
year = "2019",
publisher = "Springer",
note = "(to appear)",
url = "http://www-ictserv.poliba.it/publications/2019/ADD
RT19c"
}

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SisInf Lab - Information Systems Laboratory

Research group of Politecnico di Bari
Edoardo Orabona St, 4 Bari, Italy