标题： 保留相关性：在推荐系统中捕捉长期用户价值 显示英文标题

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

摘要： 推荐系统传统上依赖于短期参与信号，如点击和点赞，以个性化内容。 然而，这些信号通常嘈杂、稀疏，并且不足以捕捉长期用户满意度和留存率。 我们引入了Retentive Relevance，一种基于调查的内容级反馈度量方法，可以直接评估用户返回平台查看类似内容的意图。 与其他关注即时满意度的调查措施不同，Retentive Relevance针对的是前瞻性行为意图，捕捉更长期的用户意图，并提供了更强的留存预测指标。 我们使用心理测量方法验证了Retentive Relevance，确立了其收敛效度、区分效度和行为效度。 通过大规模离线建模，我们展示了Retentive Relevance在预测次日留存率方面显著优于参与信号和其他调查措施，尤其是在历史参与度有限的用户中表现更佳。 我们开发了一个可投入生产的代理模型，将Retentive Relevance集成到社交媒体平台多阶段排序系统的最终阶段。 基于该模型的校准分数调整在提升参与度和留存率的同时，减少了对低质量内容的曝光，这通过大规模A/B实验得到了验证。 这项工作提供了第一个在生产系统中将内容级用户感知与留存结果联系起来的经验验证框架。 我们提供了一个可扩展的以用户为中心的解决方案，推动了平台增长和用户体验的进步。 我们的工作对负责任的人工智能发展具有广泛的影响。 显示英文摘要

摘要： 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.