How to Make Latent Factors Interpretable by Feeding Factorization Machines with Knowledge Graphs
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Springer International Publishing
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http://link.springer.com/content/pdf/10.1007/978-3-030-30793-6_3
Reference50 articles.
1. Abdollahi, B., Nasraoui, O.: Explainable matrix factorization for collaborative filtering. In: Proceedings of the 25th International Conference on World Wide Web, WWW 2016, Montreal, Canada, 11–15 April 2016, Companion Volume, pp. 5–6 (2016)
2. Abdollahi, B., Nasraoui, O.: Explainable restricted Boltzmann machines for collaborative filtering. CoRR abs/1606.07129 (2016)
3. Adomavicius, G., Kwon, Y.O.: Multi-criteria recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 847–880. Springer, Boston, MA (2015). https://doi.org/10.1007/978-1-4899-7637-6_25
4. Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 217–253. Springer, Boston, MA (2011). https://doi.org/10.1007/978-0-387-85820-3_7
5. Anelli, V.W., Bellini, V., Di Noia, T., Bruna, W.L., Tomeo, P., Di Sciascio, E.: An analysis on time- and session-aware diversification in recommender systems. In: Bieliková, M., Herder, E., Cena, F., Desmarais, M.C. (eds.) Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, UMAP 2017, Bratislava, Slovakia, 09–12 July 2017, pp. 270–274. ACM (2017)
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