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Embarrassingly Shallow Autoencoders for Sparse Data

arXiv 1905.03375 ↗First posted 8 May 2019cs.IR cs.LG stat.MLcited by 93

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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.

Key facts. Cited by 93 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 May 2019.

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Citing papers per year: 2021: 17; 2022: 15; 2023: 25; 2024: 12; 2025: 14; 2026: 10.

93 papers.

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  1. 2023Data Distillation: A Surveycited by 86
  2. 2023Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferencescited by 66
  3. 2021SimpleX: A Simple and Strong Baseline for Collaborative Filteringcited by 65
  4. 2021How Powerful is Graph Convolution for Recommendation?cited by 50
  5. 2022Blurring-Sharpening Process Models for Collaborative Filteringcited by 35
  6. 2022CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-Attentioncited by 22
  7. 2021Revisiting the Performance of iALS on Item Recommendation Benchmarkscited by 21
  8. 2022Infinite Recommendation Networks: A Data-Centric Approachcited by 20
  9. 2023Evaluating ChatGPT as a Recommender System: A Rigorous Approachcited by 20
  10. 2023Adap-τ: Adaptively Modulating Embedding Magnitude for Recommendationcited by 19
  11. 2021Towards a Better Understanding of Linear Models for Recommendationcited by 18
  12. 2023AutoSeqRec: Autoencoder for Efficient Sequential Recommendationcited by 15
38 more
  1. 2022Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendationcited by 14
  2. 2022Breaking Feedback Loops in Recommender Systems with Causal Inferencecited by 14
  3. 2023On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top-n Recommendationcited by 13
  4. 2024From Variability to Stability: Advancing RecSys Benchmarking Practicescited by 12
  5. 2025Do LLMs Memorize Recommendation Datasets? A Preliminary Study on MovieLens-1Mcited by 12
  6. 2022Revisiting Neighborhood-based Link Prediction for Collaborative Filteringcited by 11
  7. 2021On Estimating Recommendation Evaluation Metrics under Samplingcited by 8
  8. 2021Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachabilitycited by 8
  9. 2021Trust your neighbors: A comprehensive survey of neighborhood-based methods for recommender systemscited by 8
  10. 2023Toward a Better Understanding of Loss Functions for Collaborative Filteringcited by 8
  11. 2023It's Enough: Relaxing Diagonal Constraints in Linear Autoencoders for Recommendationcited by 7
  12. 2025Graph Spectral Filtering with Chebyshev Interpolation for Recommendationcited by 7
  13. 2021Reenvisioning Collaborative Filtering vs Matrix Factorizationcited by 6
  14. 2023RecFusion: A Binomial Diffusion Process for 1D Data for Recommendationcited by 6
  15. 2023Recommendation Unlearning via Matrix Correctioncited by 6
  16. 2021Local Collaborative Autoencoderscited by 5
  17. 2023Revisiting Recommendation Loss Functions through Contrastive Learning (Technical Report)cited by 5
  18. 2023BSL: Understanding and Improving Softmax Loss for Recommendationcited by 5
  19. 2024How Fair is Your Diffusion Recommender Model?cited by 5
  20. 2024TEARS: Textual Representations for Scrutable Recommendationscited by 5
  21. 2021Session-aware Linear Item-Item Models for Session-based Recommendationcited by 4
  22. 2023When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?cited by 4
  23. 2025Yambda-5B -- A Large-Scale Multi-modal Dataset for Ranking And Retrievalcited by 4
  24. 2021Deep Variational Autoencoder with Shallow Parallel Path for Top-N Recommendation (VASP)cited by 3
  25. 2021iALS++: Speeding up Matrix Factorization with Subspace Optimizationcited by 3
  26. 2022On the Generalizability and Predictability of Recommender Systemscited by 3
  27. 2022Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendationcited by 3
  28. 2023Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysiscited by 3
  29. 2024Temporal Linear Item-Item Model for Sequential Recommendationcited by 3
  30. 2025PAC-Bayes Bounds for Multivariate Linear Regression and Linear Autoencoderscited by 3
  31. 2026Why not Collaborative Filtering in Dual View? Bridging Sparse and Dense Modelscited by 3
  32. 2022Generalization Bounds for Inductive Matrix Completion in Low-noise Settingscited by 2
  33. 2023Distributional Off-Policy Evaluation for Slate Recommendationscited by 2
  34. 2023Impression-Informed Multi-Behavior Recommender System: A Hierarchical Graph Attention Approachcited by 2
  35. 2025Recommendation Is a Dish Better Served Warmcited by 2
  36. 2025Ultra Fast Warm Start Solution for Graph Recommendationscited by 2
  37. 2021On the Regularization of Autoencoderscited by 1
  38. 2022An Evaluation Study of Generative Adversarial Networks for Collaborative Filteringcited by 1

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