Embarrassingly Shallow Autoencoders for Sparse Data

[dselivanov]

https://arxiv.org/abs/1905.03375 - should be trivial to implement

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.

[dselivanov]

Closing as this paper is garbage as it is not practical - involves inversion of the item-item similarity matrix.

• inversion of real world item-item (> 50k items) is expensive
• inverted matrix is dense

These 2 facts make model completely impractical.