Clicks Versus Conversion: Choosing a Recommender's Training Objective in E-Commerce
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Abstract (arXiv)
Ranking product recommendations to optimize for a high click-through rate (CTR) or for high conversion, such as add-to-cart rate (ACR) and Order-Submit-Rate (OSR, view-to-purchase conversion) are standard practices in e-commerce. Optimizing for CTR appears like a straightforward choice: Training data (i.e., click data) are simple to collect and often available in large quantities. Additionally, CTR is used far beyond e-commerce, making it a generalist, easily implemented option. ACR and OSR, on the other hand, are more directly linked to a shop's business goals, such as the Gross Merchandise Value (GMV). In this paper, we compare the effects of using either of these objectives using an online A/B test. Among our key findings, we demonstrate that in our shops, optimizing for OSR produces a GMV uplift more than five times larger than when optimizing for CTR, without sacrificing new product discovery. Our results also provide insights into the different feature importances for each of the objectives.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 12 references that are in the map. First posted on arXiv 14 Aug 2025.
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References · 3
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
- 2021Dive into Deep Learningcited by 111
- 2016Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendationcited by 30
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