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Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches

title
Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches
paper
(Dacrema et al., 2019)
tags
Recommender Systems, Machine Learning Papers

The authors analyzed various recent publications on recommendation systems techniques, and found that these have:

  1. Weak baselines
  2. Establish weak methods as baselines
  3. Are outperformed by simple, sometimes non-neural approaches

The simple approaches that work well include ItemKNN, a collaborative-filtering approach that uses k-nearest neighbours and item-item similarities:

\begin{equation} s_{ij} = \frac{r_i \dot r_j}{\lvert r_i \rvert \lvert r_j \rvert + h} \end{equation}

To alleviate these issues:

  1. Use appropriate evaluation methods
  2. Evaluate on appropriate datasets (size is important)
  3. Release reproducible code

Bibliography

Dacrema, M. F., Cremonesi, P., & Jannach, D., Are we really making much progress? a worrying analysis of recent neural recommendation approaches, CoRR, (), (2019).

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