An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy

TL;DR

This paper presents an ensemble learning system for Etsy's CTR prediction that combines historical, behavioral, and content-based signals using deep learning and multimodal techniques, improving accuracy in a large-scale e-commerce setting.

Contribution

It introduces a novel ensemble approach integrating deep learning for text and image features, tailored for Etsy's promoted listings CTR prediction system.

Findings

The system outperforms non-trivial baselines on Etsy's dataset.

Strong correlation between offline metrics and online performance.

First technical overview of such a system in e-commerce.

Abstract

Etsy is a global marketplace where people across the world connect to make, buy and sell unique goods. Sellers at Etsy can promote their product listings via advertising campaigns similar to traditional sponsored search ads. Click-Through Rate (CTR) prediction is an integral part of online search advertising systems where it is utilized as an input to auctions which determine the final ranking of promoted listings to a particular user for each query. In this paper, we provide a holistic view of Etsy's promoted listings' CTR prediction system and propose an ensemble learning approach which is based on historical or behavioral signals for older listings as well as content-based features for new listings. We obtain representations from texts and images by utilizing state-of-the-art deep learning techniques and employ multimodal learning to combine these different signals. We compare the system to non-trivial baselines on a large-scale real world dataset from Etsy, demonstrating the effectiveness of the model and strong correlations between offline experiments and online performance. The paper is also the first technical overview to this kind of product in e-commerce context.