An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy
This addresses the problem of improving advertising relevance for sellers and users on e-commerce platforms like Etsy, though it is incremental as it builds on existing ensemble and multimodal techniques.
The paper tackles click-through rate prediction for promoted listings on Etsy by proposing an ensemble learning approach that combines historical and content-based features using multimodal deep learning, demonstrating effectiveness with strong correlations between offline and online performance on a large-scale real-world dataset.
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.