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arXiv · 2601.02807

COFFEE: COdesign Framework for Feature Enriched Embeddings in Ads-Ranking Systems

Sohini Roychowdhury·Doris Wang·Qian Ge·Joy Mu·Srihari Reddy
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Abstract

Diverse and enriched data sources are essential for commercial ads-recommendation models to accurately assess user interest both before and after engagement with content. While extended user-engagement histories can improve the prediction of user interests, it is equally important to embed activity sequences from multiple sources to ensure freshness of user and ad-representations, following scaling law principles. In this paper, we present a novel three-dimensional framework for enhancing user-ad representations without increasing model inference or serving complexity. The first dimension examines the impact of incorporating diverse event sources, the second considers the benefits of longer user histories, and the third focuses on enriching data with additional event attributes and multi-modal embeddings. We assess the return on investment (ROI) of our source enrichment framework by comparing organic user engagement sources, such as content viewing, with ad-impression sources. The proposed method can boost the area under curve (AUC) and the slope of scaling curves for ad-impression sources by 1.56 to 2 times compared to organic usage sources even for short online-sequence lengths of 100 to 10K. Additionally, click-through rate (CTR) prediction improves by 0.56% AUC over the baseline production ad-recommendation system when using enriched ad-impression event sources, leading to improved sequence scaling resolutions for longer and offline user-ad representations.

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Sohini Roychowdhury, Doris Wang, Qian Ge, Joy Mu, Srihari Reddy. 2026-01-06. COFFEE: COdesign Framework for Feature Enriched Embeddings in Ads-Ranking Systems. https://arxiv.org/abs/2601.02807

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