Embarrassingly Shallow Autoencoders for Sparse Data

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.

In the proceedings of the Web Conference (WWW) 2019 (7 pages)

References in corpus (6)

• High-dimensional graphs and variable selection with the Lasso
• Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
• Session-based Recommendations with Recurrent Neural Networks
• A Neural Autoregressive Approach to Collaborative Filtering
• Iterative Thresholding Algorithm for Sparse Inverse Covariance Estimation
• Collaborative Filtering via High-Dimensional Regression

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• Toward a Better Understanding of Loss Functions for Collaborative Filtering
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• From Variability to Stability: Advancing RecSys Benchmarking Practices
• Revisiting Neighborhood-based Link Prediction for Collaborative Filtering
• Do Offline Metrics Predict Online Performance in Recommender Systems?
• Fair Augmentation for Graph Collaborative Filtering
• Measuring the User Satisfaction in a Recommendation Interface with Multiple Carousels
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• Collaborative Residual Metric Learning
• Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation
• A Worrying Reproducibility Study of Intent-Aware Recommendation Models
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• Recommendation Is a Dish Better Served Warm
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• The Hidden Cost of Defaults in Recommender System Evaluation
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• Accurate and Diverse Recommendations via Propensity-Weighted Linear Autoencoders