Retentive Relevance: Capturing Long-Term User Value in Recommendation Systems

• URL: http://arxiv.org/abs/2510.07621v1
• Date: Wed, 08 Oct 2025 23:38:57 GMT
• Title: Retentive Relevance: Capturing Long-Term User Value in Recommendation Systems
• Authors: Saeideh Bakhshi, Phuong Mai Nguyen, Robert Schiller, Tiantian Xu, Pawan Kodandapani, Andrew Levine, Cayman Simpson, Qifan Wang,
• Abstract summary: We introduce Retentive Relevance, a novel content-level survey-based feedback measure.<n>Retentive Relevance directly assesses users' intent to return to the platform for similar content.<n>We show that Retentive Relevance significantly outperforms both engagement signals and other survey measures in predicting next-day retention.
• Score: 29.596401271139797
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recommendation systems have traditionally relied on short-term engagement signals, such as clicks and likes, to personalize content. However, these signals are often noisy, sparse, and insufficient for capturing long-term user satisfaction and retention. We introduce Retentive Relevance, a novel content-level survey-based feedback measure that directly assesses users' intent to return to the platform for similar content. Unlike other survey measures that focus on immediate satisfaction, Retentive Relevance targets forward-looking behavioral intentions, capturing longer term user intentions and providing a stronger predictor of retention. We validate Retentive Relevance using psychometric methods, establishing its convergent, discriminant, and behavioral validity. Through large-scale offline modeling, we show that Retentive Relevance significantly outperforms both engagement signals and other survey measures in predicting next-day retention, especially for users with limited historical engagement. We develop a production-ready proxy model that integrates Retentive Relevance into the final stage of a multi-stage ranking system on a social media platform. Calibrated score adjustments based on this model yield substantial improvements in engagement, and retention, while reducing exposure to low-quality content, as demonstrated by large-scale A/B experiments. This work provides the first empirically validated framework linking content-level user perceptions to retention outcomes in production systems. We offer a scalable, user-centered solution that advances both platform growth and user experience. Our work has broad implications for responsible AI development.

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