Papers › Embarrassingly Shallow Autoencoders for Sparse Data

Embarrassingly Shallow Autoencoders for Sparse Data

8 May 2019arXiv:1905.03375archive 2025-07-28

Harald Steck

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.

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