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

arXiv: https://arxiv.org/abs/1711.01377 · HTML: https://mlsanity.com/paper/1711.01377/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2017.

## Built on by (most cited first, 4 of 4)

- [User Response Prediction in Online Advertising](https://mlsanity.com/paper/2101.02342/) (2021)
- [Click prediction boosting via Bayesian hyperparameter optimization based ensemble learning pipelines](https://mlsanity.com/paper/2206.03592/) (2022)
- [Learning to Rank Normalized Entropy Curves with Differentiable Window Transformation](https://mlsanity.com/paper/2301.10443/) (2023)
- [Marginal Expected Revenue for Jointly Ranking Auction and Fixed-Price Listings in E-Commerce Sponsored Search](https://mlsanity.com/paper/2609.01628/) (2026)

## Abstract (arXiv)

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.