# From Clicks to Conversions: Recommendation for long-term reward

arXiv: https://arxiv.org/abs/2009.00497 · HTML: https://mlsanity.com/paper/2009.00497/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2020.

## Built on by (most cited first, 1 of 1)

- [Extending Open Bandit Pipeline to Simulate Industry Challenges](https://mlsanity.com/paper/2209.04147/) (2022)

## Abstract (arXiv)

Recommender systems are often optimised for short-term reward: a recommendation is considered successful if a reward (e.g. a click) can be observed immediately after the recommendation. The advantage of this framework is that with some reasonable (although questionable) assumptions, it allows familiar supervised learning tools to be used for the recommendation task. However, it means that long-term business metrics, e.g. sales or retention are ignored. In this paper we introduce a framework for modeling long-term rewards in the RecoGym simulation environment. We use this newly introduced functionality to showcase problems introduced by the last-click attribution scheme in the case of conversion-optimized recommendations and propose a simple extension that leads to state-of-the-art results.