Causal Discovery in Recommender Systems: Example and Discussion
This addresses the problem of understanding causal relationships in recommender systems for AI/ML researchers, but it is incremental as it provides an example rather than a novel method.
The paper tackled the problem of causal discovery in recommender systems by modeling it with causal graphs using observational data and prior knowledge, resulting in a graph showing that only a few variables influence feedback signals, contrasting with trends of using massive models.
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.