How to Make Latent Factors Interpretable by Feeding Factorization Machines with Knowledge Graphs

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.

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Authors and Affiliations

1. Polytechnic University of Bari, Bari, Italy Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio & Joseph Trotta
2. Milan, Italy Azzurra Ragone

1. Vito Walter Anelli

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Editors and Affiliations

1. Fondazione Bruno Kessler, Trento, Italy Chiara Ghidini
2. Linköping University, Linköping, Sweden Olaf Hartig
3. University of Bonn, Bonn, Germany Maria Maleshkova
4. University of Economics Prague, Prague, Czech Republic Vojtěch Svátek
5. University of Illinois at Chicago, Chicago, IL, USA Isabel Cruz
6. University of Chile, Santiago, Chile Aidan Hogan
7. Memect Technology, Beijing, China Jie Song
8. Mines Saint-Etienne, Saint-Etienne, France Maxime Lefrançois
9. Inria Sophia Antipolis - Méditerranée, Sophia Antipolis, France Fabien Gandon

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Anelli, V.W., Di Noia, T., Di Sciascio, E., Ragone, A., Trotta, J. (2019). How to Make Latent Factors Interpretable by Feeding Factorization Machines with Knowledge Graphs. In: Ghidini, C., et al. The Semantic Web – ISWC 2019. ISWC 2019. Lecture Notes in Computer Science(), vol 11778. Springer, Cham. https://doi.org/10.1007/978-3-030-30793-6_3

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