Neural networks for personalized item rankings.

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]