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

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Book cover The Semantic Web – ISWC 2019 (ISWC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11778))

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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.

Authors are listed in alphabetical order.

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Notes

  1. 1.

    http://research.yahoo.com/Academic_Relations.

  2. 2.

    http://research.yahoo.com/Academic_Relations.

  3. 3.

    https://2015.eswc-conferences.org/program/semwebeval.html.

  4. 4.

    https://github.com/sisinflab/LinkedDatasets/.

  5. 5.

    https://github.com/sisinflab/HybridFactorizationMachines.

  6. 6.

    Results of the full experiments: https://github.com/sisinflab/papers-results/tree/master/kahfm-results/.

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Author information

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

Authors
  1. Vito Walter Anelli
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  2. Tommaso Di Noia
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  3. Eugenio Di Sciascio
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  4. Azzurra Ragone
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  5. Joseph Trotta
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Corresponding author

Correspondence to Vito Walter Anelli .

Editor information

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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  • DOI: https://doi.org/10.1007/978-3-030-30793-6_3

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