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Predicting delayed outcomes is an important problem in recommender systems(e.g., if customers will finish reading an ebook). We formalize the problem asan adversarial, delayed online learning problem and consider how a proxy forthe delayed outcome (e.g., if customers read a third of the book in 24 hours)can help minimize regret, even though the proxy is not available when making aprediction. Motivated by our regret analysis, we propose two neural networkarchitectures: Factored Forecaster (FF) which is ideal if the proxy isinformative of the outcome in hindsight, and Residual Factored Forecaster (RFF)that is robust to a non-informative proxy. Experiments on two real-worlddatasets for predicting human behavior show that RFF outperforms both FF and adirect forecaster that does not make use of the proxy. Our results suggest thatexploiting proxies by factorization is a promising way to mitigate the impactof long delays in human-behavior prediction tasks.

Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems