Embarrassingly Shallow Autoencoders for Sparse Data
This work addresses the challenge of improving recommendation systems for sparse data, though it is incremental as it combines existing elements.
The authors tackled the problem of collaborative filtering for sparse implicit feedback data by proposing a linear model with a closed-form training solution, achieving better ranking accuracy than state-of-the-art methods on most public datasets.
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.