Back to Search
Start Over
Neural networks for personalized item rankings.
- Source :
-
Neurocomputing . May2019, Vol. 342, p60-65. 6p. - Publication Year :
- 2019
-
Abstract
- Most users typically interact with products only through implicit feedback such as clicks or purchases rather than explicit user-provided information like product ratings. Learning to rank products according to individual preferences using only implicit feedback can be helpful to make useful recommendations. In this paper, a neural network architecture to solve collaborative filtering problems for personalized rankings on implicit feedback datasets is presented. It is shown how a layer of constant weights forces the network to learn pairwise item rankings. Additionally, similarities between the proposed neural network and a matrix factorization model trained with the Bayesian Personalized Ranking optimization criterion are proven. The experiments indicate state-of-the-art performance for the task of personalized ranking. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 342
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 136345199
- Full Text :
- https://doi.org/10.1016/j.neucom.2018.10.083