Priors for Diversity and Novelty on Neural Recommender Systems

  1. Alfonso Landin 1
  2. Daniel Valcarce
  3. Javier Parapar 1
  4. Álvaro Barreiro
  1. 1 Universidade da Coruña
    info

    Universidade da Coruña

    La Coruña, España

    ROR https://ror.org/01qckj285

Libro:
XoveTIC 2019: The 2nd XoveTIC Conference (XoveTIC 2019), A Coruña, Spain, 5–6 September
  1. Alberto Alvarellos González (ed. lit.)
  2. José Joaquim de Moura Ramos (ed. lit.)
  3. Beatriz Botana Barreiro (ed. lit.)
  4. Javier Pereira Loureiro (ed. lit.)
  5. Manuel F. González Penedo (ed. lit.)

Editorial: MDPI

ISBN: 978-3-03921-444-0,  978-3-03921-443-3

Año de publicación: 2019

Congreso: XoveTIC (2. 2019. A Coruña)

Tipo: Aportación congreso

DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Resumen

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 interms 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 desiredproperties, 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.

Fuente de los datos: Dialnet