# Rank-to-engage: New Listwise Approaches to Maximize Engagement

## TL;DR

This paper introduces novel listwise learning-to-rank methods to maximize engagement metrics without requiring desired rankings during training, demonstrating improved performance on synthetic and real-world news article ranking tasks.

## Contribution

It proposes two new approaches for listwise ranking without observed desired orders, including an extension of ListMLE and a general machine learning framework with item-payoff and positional-gain models.

## Key findings

- Enhanced ranking performance over traditional ListMLE
- Effective in synthetic and real-world news ranking scenarios
- Addresses challenges of learning without explicit desired rankings

## Abstract

For many internet businesses, presenting a given list of items in an order that maximizes a certain metric of interest (e.g., click-through-rate, average engagement time etc.) is crucial. We approach the aforementioned task from a learning-to-rank perspective which reveals a new problem setup. In traditional learning-to-rank literature, it is implicitly assumed that during the training data generation one has access to the \emph{best or desired} order for the given list of items. In this work, we consider a problem setup where we do not observe the desired ranking. We present two novel solutions: the first solution is an extension of already existing listwise learning-to-rank technique--Listwise maximum likelihood estimation (ListMLE)--while the second one is a generic machine learning based framework that tackles the problem in its entire generality. We discuss several challenges associated with this generic framework, and propose a simple \emph{item-payoff} and \emph{positional-gain} model that addresses these challenges. We provide training algorithms, inference procedures, and demonstrate the effectiveness of the two approaches over traditional ListMLE on synthetic as well as on real-life setting of ranking news articles for increased dwell time.