# Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems

arXiv: https://arxiv.org/abs/1807.09387 · HTML: https://mlsanity.com/paper/1807.09387/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2018.

## Built on by (most cited first, 2 of 2)

- [Learning to Bid Long-Term: Multi-Agent Reinforcement Learning with Long-Term and Sparse Reward in Repeated Auction Games](https://mlsanity.com/paper/2204.02268/) (2022)
- [Multi-Agent Reinforcement Learning for Long-Term Network Resource Allocation through Auction: a V2X Application](https://mlsanity.com/paper/2208.04237/) (2022)

## Abstract (arXiv)

Predicting delayed outcomes is an important problem in recommender systems (e.g., if customers will finish reading an ebook). We formalize the problem as an adversarial, delayed online learning problem and consider how a proxy for the 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 a prediction. Motivated by our regret analysis, we propose two neural network architectures: Factored Forecaster (FF) which is ideal if the proxy is informative of the outcome in hindsight, and Residual Factored Forecaster (RFF) that is robust to a non-informative proxy. Experiments on two real-world datasets for predicting human behavior show that RFF outperforms both FF and a direct forecaster that does not make use of the proxy. Our results suggest that exploiting proxies by factorization is a promising way to mitigate the impact of long delays in human-behavior prediction tasks.