Causal Discovery in Recommender Systems: Example and Discussion

TL;DR

This paper demonstrates how causal discovery can be applied to recommender systems using observational data and prior knowledge, revealing key influential variables and contrasting with large-scale neural network approaches.

Contribution

It provides a practical example of causal graph modeling in recommender systems and highlights the importance of variable selection over model complexity.

Findings

Few variables significantly influence feedback signals

Causal graph differs from large neural network models

Prior knowledge aids causal discovery

Abstract

Causality is receiving increasing attention by the artificial intelligence and machine learning communities. This paper gives an example of modelling a recommender system problem using causal graphs. Specifically, we approached the causal discovery task to learn a causal graph by combining observational data from an open-source dataset with prior knowledge. The resulting causal graph shows that only a few variables effectively influence the analysed feedback signals. This contrasts with the recent trend in the machine learning community to include more and more variables in massive models, such as neural networks.