Embarrassingly Shallow Autoencoders for Sparse Data

Code

• github.com/PreferredAI/cornac
  tf★ 1,024
• github.com/AmazingDD/daisyRec
  pytorch★ 550
• github.com/recsys-benchmark/daisyrec-v2.0
  pytorch★ 65
• github.com/glami/sansa
  none★ 45
• github.com/franckjay/TorchEASE
  pytorch★ 0
• github.com/MindSpore-scientific/code-10/tree/main/shallow-rnns
  mindspore★ 0
• github.com/Darel13712/ease_rec
  none★ 0
• github.com/jvbalen/autoencoders_cf
  pytorch★ 0
• github.com/AhmadRK94/NeuEASE
  pytorch★ 0

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.

Tasks

Benchmark Results

Reproductions