In the Eye of the Beholder: Robust Prediction with Causal User Modeling
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 relevan...
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Zusammenfassung: | 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. |
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DOI: | 10.48550/arxiv.2206.00416 |