# Click-aware purchase prediction with push at the top

## TL;DR

This paper introduces P3Stop, a robust learning-to-rank method that leverages click data to improve purchase prediction accuracy, especially for top-ranked items, outperforming existing methods on real-world datasets.

## Contribution

The paper proposes a novel ranking model, P3Stop, designed to effectively utilize click records for purchase prediction, addressing the unreliability of click data compared to purchase data.

## Key findings

- P3Stop significantly outperforms state-of-the-art methods on real datasets.
- The model is particularly effective for top-ranked item prediction.
- Leveraging click data improves purchase prediction accuracy.

## Abstract

Eliciting user preferences from purchase records for performing purchase prediction is challenging because negative feedback is not explicitly observed, and because treating all non-purchased items equally as negative feedback is unrealistic. Therefore, in this study, we present a framework that leverages the past click records of users to compensate for the missing user-item interactions of purchase records, i.e., non-purchased items. We begin by formulating various model assumptions, each one assuming a different order of user preferences among purchased, clicked-but-not-purchased, and non-clicked items, to study the usefulness of leveraging click records. We implement the model assumptions using the Bayesian personalized ranking model, which maximizes the area under the curve for bipartite ranking. However, we argue that using click records for bipartite ranking needs a meticulously designed model because of the relative unreliableness of click records compared with that of purchase records. Therefore, we ultimately propose a novel learning-to-rank method, called P3Stop, for performing purchase prediction. The proposed model is customized to be robust to relatively unreliable click records by particularly focusing on the accuracy of top-ranked items. Experimental results on two real-world e-commerce datasets demonstrate that P3STop considerably outperforms the state-of-the-art implicit-feedback-based recommendation methods, especially for top-ranked items.