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In the Eye of the Beholder: Robust Prediction with Causal User Modeling

Amir Feder · Guy Horowitz · Yoav Wald · Roi Reichart · Nir Rosenfeld

Hall J (level 1) #409

Keywords: [ bounded rationality ] [ causal representation learning ] [ Behavioral User Modeling ] [ Robust Prediction ]


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 \emph{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.

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