In the Eye of the Beholder: Robust Prediction with Causal User Modeling
This addresses robustness to distributional changes for social platforms, but it is incremental as it builds on causal modeling approaches.
The paper tackles the problem of robust relevance prediction in dynamic environments like recommendation systems by modeling users as boundedly-rational decision makers with causal beliefs, and experiments show its effectiveness.
Accurately predicting the relevance of items to users is crucial to the success of many social platforms. Conventional approaches train models on logged historical data; but recommendation systems, media services, and online marketplaces all exhibit a constant influx of new content -- making relevancy a moving target, to which standard predictive models are not robust. In this paper, we propose a learning framework for relevance prediction that is robust to changes in the data distribution. Our key observation is that robustness can be obtained by accounting for how users causally perceive the environment. We model users as boundedly-rational decision makers whose causal beliefs are encoded by a causal graph, and show how minimal information regarding the graph can be used to contend with distributional changes. Experiments in multiple settings demonstrate the effectiveness of our approach.