# Signed Distance-based Deep Memory Recommender

## TL;DR

This paper introduces a deep learning recommender system that captures complex non-linear user-item relationships using signed distance measures, significantly outperforming existing models on multiple real-world datasets.

## Contribution

The paper proposes a novel deep memory recommender leveraging signed distance to model non-linear user-item interactions explicitly and implicitly.

## Key findings

- Achieved significant improvements over ten state-of-the-art models.
- Performed well in both general and shopping basket-based recommendation tasks.
- Validated on six real-world datasets.

## Abstract

Personalized recommendation algorithms learn a user's preference for an item by measuring a distance/similarity between them. However, some of the existing recommendation models (e.g., matrix factorization) assume a linear relationship between the user and item. This approach limits the capacity of recommender systems, since the interactions between users and items in real-world applications are much more complex than the linear relationship. To overcome this limitation, in this paper, we design and propose a deep learning framework called Signed Distance-based Deep Memory Recommender, which captures non-linear relationships between users and items explicitly and implicitly, and work well in both general recommendation task and shopping basket-based recommendation task. Through an extensive empirical study on six real-world datasets in the two recommendation tasks, our proposed approach achieved significant improvement over ten state-of-the-art recommendation models.