# Embarrassingly Shallow Autoencoders for Sparse Data

## TL;DR

This paper introduces a simple linear autoencoder model tailored for sparse data in recommender systems, demonstrating superior ranking accuracy over complex models through a closed-form solution.

## Contribution

The paper presents a novel shallow autoencoder with a closed-form training solution that outperforms state-of-the-art deep models on sparse implicit feedback data.

## Key findings

- Achieves better ranking accuracy than deep non-linear models.
- Provides a closed-form solution for training.
- Effective on multiple publicly available datasets.

## Abstract

Combining simple elements from the literature, we define a linear model that is geared toward sparse data, in particular implicit feedback data for recommender systems. We show that its training objective has a closed-form solution, and discuss the resulting conceptual insights. Surprisingly, this simple model achieves better ranking accuracy than various state-of-the-art collaborative-filtering approaches, including deep non-linear models, on most of the publicly available data-sets used in our experiments.