# NeuRec: On Nonlinear Transformation for Personalized Ranking

arXiv: https://arxiv.org/abs/1805.03002 · HTML: https://mlsanity.com/paper/1805.03002/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2018.

## Built on by (most cited first, 1 of 1)

- [On the Generalizability and Predictability of Recommender Systems](https://mlsanity.com/paper/2206.11886/) (2022)

## Abstract (arXiv)

Modeling user-item interaction patterns is an important task for personalized recommendations. Many recommender systems are based on the assumption that there exists a linear relationship between users and items while neglecting the intricacy and non-linearity of real-life historical interactions. In this paper, we propose a neural network based recommendation model (NeuRec) that untangles the complexity of user-item interactions and establishes an integrated network to combine non-linear transformation with latent factors. We further design two variants of NeuRec: user-based NeuRec and item-based NeuRec, by concentrating on different aspects of the interaction matrix. Extensive experiments on four real-world datasets demonstrated their superior performances on personalized ranking task.