Priors for Diversity and Novelty on Neural Recommender Systems
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Priors for Diversity and Novelty on Neural Recommender SystemsFecha
2019-07-31Cita bibliográfica
LANDIN, Alfonso, et al. Priors for Diversity and Novelty on Neural Recommender Systems. En Multidisciplinary Digital Publishing Institute Proceedings. 2019. p. 20.
Resumen
[Abstract] PRIN is a neural based recommendation method that allows the incorporation of item prior information into the recommendation process. In this work we study how the system behaves in terms of novelty and diversity under different configurations of item prior probability estimations. Our results show the versatility of the framework and how its behavior can be adapted to the desired properties, whether accuracy is preferred or diversity and novelty are the desired properties, or how a balance can be achieved with the proper selection of prior estimations.
Palabras clave
Recommender systems
Neural models
Item priors
Diversity
Novelty
Neural models
Item priors
Diversity
Novelty
Versión del editor
Derechos
Atribución 4.0 España
ISSN
2504-3900