Personalized Advertisement Recommendation: A Ranking Approach to Address the Ubiquitous Click Sparsity Problem

TL;DR

This paper addresses the challenge of extreme click sparsity in personalized ad recommendation systems by proposing a ranking-based policy optimized with AUC loss, demonstrating significant improvements over classifier-based methods through extensive experiments.

Contribution

It introduces a ranking-based policy approach using AUC optimization to mitigate click sparsity issues in personalized ad recommendation systems, outperforming traditional classifier-based policies.

Findings

Ranking-based policies improve click-through rates.

AUC optimization alleviates click sparsity problems.

Experimental results show significant CTR gains.

Abstract

We study the problem of personalized advertisement recommendation (PAR), which consist of a user visiting a system (website) and the system displaying one of $K$ ads to the user. The system uses an internal ad recommendation policy to map the user's profile (context) to one of the ads. The user either clicks or ignores the ad and correspondingly, the system updates its recommendation policy. PAR problem is usually tackled by scalable \emph{contextual bandit} algorithms, where the policies are generally based on classifiers. A practical problem in PAR is extreme click sparsity, due to very few users actually clicking on ads. We systematically study the drawback of using contextual bandit algorithms based on classifier-based policies, in face of extreme click sparsity. We then suggest an alternate policy, based on rankers, learnt by optimizing the Area Under the Curve (AUC) ranking loss, which can significantly alleviate the problem of click sparsity. We conduct extensive experiments on public datasets, as well as three industry proprietary datasets, to illustrate the improvement in click-through-rate (CTR) obtained by using the ranker-based policy over classifier-based policies.