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Gomez Vilda, PedroBenavente Peces, CesarAlvarez Marquina, AgustinCaiza Guanochanga, Gustavo JavierMartinez Olalla, RafaelMateos Caballero, AlfonsoMitrana, VictorAmador Guerra, JulioMuÑoz Ramirez, EncarnacionBedoya Frutos, CesarGil Lopez, TomasLatorre De La Fuente, AntonioArroyo Montoro, FernandoGomez Aguilera, Enrique JavierCaballero Cuesta, AmadorLauret Aguirregabiria, BenitoBriso Rodriguez, CesarGomez Rodellar, AndresHuerta Gomez De Merodio, M. ConsueloRodellar Biarge, M. VictoriaAlonso Villaverde, SantiagoOropesa Garcia, IgnacioBobadilla Sancho, JesusGutierrez Rodriguez, AbrahamViana Matesanz, MiguelClemente Jul, M. Del CarmenTena Ramos, DavidSanchez Gonzalez, PatriciaFernandez Martinez, FernandoPaun, Paul AndreiGalvez Huerta, Miguel AngelAlonso Amo, FernandoMorillas Guerrero, Juan JoseLópez Olocco, TomásRequelme, Narcisa De JesusArias LondoÑo, Julian DavidSmart Innovation, Systems and Technologies
Publicado en: - (), DOI: 21903018
Autores: BRISO RODRIGUEZ, CESAR
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Resumen
Neural Collaborative Filtering recommendations are traditionally based on regression architectures (returning continuous predictions, e.g. 2.8 stars), such as DeepMF and NCF. However, there are advantages in the use of collaborative filtering classification models. This work tested both neuronal approaches using a set of representative open datasets, baselines, and quality measures. The results show the superiority of the regular regression model compared to the regular classification model (returning discrete predictions, e.g. 1–5 stars) and the binary classification model (returning binary predictions: recommended, non-recommended). Results also show a similar performance when comparing our proposed recommendation neural approach with the state-of-the-art neural regression baseline. The key issue is the additional information the recommendation approach provides compared to the regression model: While the regression baseline only returns the recommendation values, the proposed recommendation model returns ⟨ value, probability ⟩ pairs. Extra probability information can be used in the recommender systems area for different objectives: recommendation explanation, visualization of results, quality improvements, mitigate attack risks, etc. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Este trabajo se ha realizado con colaboración internacional, concretamente con investigadores de: Germany; United States of America.
Existe un liderazgo significativo ya que algunos de los autores pertenecientes a la institución aparecen como primer o último firmante, se puede apreciar en el detalle: Último Autor ().