研究提出可量化的好奇心度量并用于推荐系统
When Does a Recommendation Spark Curiosity? Theory, Measurement, and Field Evidence
这篇把'好奇心'从心理学概念变成了能算的公式，还接进了真实推荐系统。离线实验打赢了SOTA，线上A/B测试让点击率和观看时长都涨了，现在已经在亚洲某视频平台跑着。做推荐算法的、研究用户参与度的人都该看看这套建模思路。
一项新研究将信息缺口理论形式化为好奇心的五条公理，构建了可计算的推荐物品好奇心度量。问卷研究中，该度量与用户自我报告的好奇心吻合，表现优于既有代理指标。
研究者据此设计了好奇心感知的推荐系统，把该度量纳入效用函数，在三个真实数据集的离线实验中显著超越当前最优方法。研究团队随后在一家亚洲大型视频流媒体平台开展大规模在线对照实验，相比生产系统提升了点击率、视频观看量和观看时长，并已正式上线部署。
When Does a Recommendation Spark Curiosity? Theory, Measurement, and Field Evidence
Curiosity, the drive to close the gap between what one knows and what one wants to know, pulls consumers beyond their familiar content, sustains engagement, and breaks them out of the filter bubbles of repetitive, relevance-only recommendations. Despite its importance, curiosity is hardly incorporated into modern recommender systems for lack of a formal measure. The information-gap theory of curiosity provides no formal quantitative model that a recommender system can operate on, and existing proxy metrics do not accurately measure the curiosity that a recommended item evokes. To fill this research gap, we formalize the information-gap theory into five axioms of curiosity and construct the canonical formulation of a computational measure of the curiosity that a recommended item evokes in a consumer. We validate our curiosity measure through a survey-based study, in which it matches consumers' self-reported curiosity and outperforms alternative proxy metrics. We subsequently build a curiosity-aware recommender system that incorporates this measure into the utility function design, and we show through offline experiments on three real-world datasets that it significantly outperforms the state-of-the-art methods. Furthermore, we conduct a large-scale online controlled experiment at a major video-streaming platform in Asia, where we show that our method significantly increases click-through rate, video views, and time spent over the latest production system, leading to tangible economic impact for the company. Our method has since been deployed on the platform and remains in operation. Our findings establish consumer curiosity as a measurable and actionable objective for recommender systems, with important managerial implications for digital platforms to recommend what consumers wonder about.
This report is categorized under AITOP's "Papers" desk. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include 推荐系统, 信息缺口理论, Curiosity, Recommender System.
Source attribution: curated from arXiv cs.LG (@Pan Li, Alexander Tuzhilin).
