# Embarrassingly Shallow Autoencoders for Sparse Data

arXiv: https://arxiv.org/abs/1905.03375 · HTML: https://mlsanity.com/paper/1905.03375/

## Key facts

- Cited by 94 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2019.

## Built on by (most cited first, 30 of 94)

- [Data Distillation: A Survey](https://mlsanity.com/paper/2301.04272/) (2023)
- [Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences](https://mlsanity.com/paper/2307.14225/) (2023)
- [SimpleX: A Simple and Strong Baseline for Collaborative Filtering](https://mlsanity.com/paper/2109.12613/) (2021)
- [How Powerful is Graph Convolution for Recommendation?](https://mlsanity.com/paper/2108.07567/) (2021)
- [Blurring-Sharpening Process Models for Collaborative Filtering](https://mlsanity.com/paper/2211.09324/) (2022)
- [CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-Attention](https://mlsanity.com/paper/2204.06519/) (2022)
- [Revisiting the Performance of iALS on Item Recommendation Benchmarks](https://mlsanity.com/paper/2110.14037/) (2021)
- [Infinite Recommendation Networks: A Data-Centric Approach](https://mlsanity.com/paper/2206.02626/) (2022)
- [Evaluating ChatGPT as a Recommender System: A Rigorous Approach](https://mlsanity.com/paper/2309.03613/) (2023)
- [Adap-τ: Adaptively Modulating Embedding Magnitude for Recommendation](https://mlsanity.com/paper/2302.04775/) (2023)
- [Towards a Better Understanding of Linear Models for Recommendation](https://mlsanity.com/paper/2105.12937/) (2021)
- [AutoSeqRec: Autoencoder for Efficient Sequential Recommendation](https://mlsanity.com/paper/2308.06878/) (2023)
- [Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendation](https://mlsanity.com/paper/2202.04920/) (2022)
- [Breaking Feedback Loops in Recommender Systems with Causal Inference](https://mlsanity.com/paper/2207.01616/) (2022)
- [On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top-n Recommendation](https://mlsanity.com/paper/2307.15053/) (2023)
- [From Variability to Stability: Advancing RecSys Benchmarking Practices](https://mlsanity.com/paper/2402.09766/) (2024)
- [Do LLMs Memorize Recommendation Datasets? A Preliminary Study on MovieLens-1M](https://mlsanity.com/paper/2505.10212/) (2025)
- [Revisiting Neighborhood-based Link Prediction for Collaborative Filtering](https://mlsanity.com/paper/2203.15789/) (2022)
- [On Estimating Recommendation Evaluation Metrics under Sampling](https://mlsanity.com/paper/2103.01474/) (2021)
- [Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability](https://mlsanity.com/paper/2107.00833/) (2021)
- [Trust your neighbors: A comprehensive survey of neighborhood-based methods for recommender systems](https://mlsanity.com/paper/2109.04584/) (2021)
- [Toward a Better Understanding of Loss Functions for Collaborative Filtering](https://mlsanity.com/paper/2308.06091/) (2023)
- [It's Enough: Relaxing Diagonal Constraints in Linear Autoencoders for Recommendation](https://mlsanity.com/paper/2305.12922/) (2023)
- [RecFusion: A Binomial Diffusion Process for 1D Data for Recommendation](https://mlsanity.com/paper/2306.08947/) (2023)
- [Graph Spectral Filtering with Chebyshev Interpolation for Recommendation](https://mlsanity.com/paper/2505.00552/) (2025)
- [Reenvisioning Collaborative Filtering vs Matrix Factorization](https://mlsanity.com/paper/2107.13472/) (2021)
- [Recommendation Unlearning via Matrix Correction](https://mlsanity.com/paper/2307.15960/) (2023)
- [Local Collaborative Autoencoders](https://mlsanity.com/paper/2103.16103/) (2021)
- [Revisiting Recommendation Loss Functions through Contrastive Learning (Technical Report)](https://mlsanity.com/paper/2312.08520/) (2023)
- [BSL: Understanding and Improving Softmax Loss for Recommendation](https://mlsanity.com/paper/2312.12882/) (2023)

## Abstract (arXiv)

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.