As the popularity of video streaming entertainment continues to grow, understanding how users engage with the content and react to its changes becomes a critical success factor for every stakeholder. User engagement, i.e., the percentage of video the user watches before quitting, is central to customer loyalty, content personalization, ad relevance, and A/B testing. This paper presents DIGITWISE, a digital twin-based approach for modeling adaptive video streaming engagement. Traditional adaptive bitrate (ABR) algorithms assume that all users react similarly to video streaming artifacts and network issues, neglecting individual user sensitivities. DIGITWISE leverages the concept of a digital twin, a digital replica of a physical entity, to model user engagement based on past viewing sessions. The digital twin receives input about streaming events and utilizes supervised machine learning to predict user engagement for a given session. The system model consists of a data processing pipeline, machine learning models acting as digital twins, and a unified model to predict engagement. DIGITWISE employs the XGBoost model in both digital twins and unified models. The proposed architecture demonstrates the importance of personal user sensitivities, reducing user engagement prediction error by up to 5.8% compared to non-user-aware models. Furthermore, DIGITWISE can optimize content provisioning and delivery by identifying the features that maximize engagement, providing an average engagement increase of up to 8.6%.

翻译：随着视频流媒体娱乐的日益普及，理解用户如何参与内容并对其变化作出反应，已成为所有利益相关者取得成功的关键因素。用户参与度，即用户在退出前观看视频的百分比，对于客户忠诚度、内容个性化、广告相关性以及A/B测试至关重要。本文提出了DIGITWISE，一种基于数字孪生的自适应视频流媒体参与度建模方法。传统的自适应比特率（ABR）算法假设所有用户对视频流媒体伪影和网络问题的反应相似，忽略了用户的个体敏感性。DIGITWISE利用数字孪生（物理实体的数字副本）的概念，基于过往观看会话对用户参与度进行建模。该数字孪生接收关于流媒体事件的输入，并利用监督式机器学习来预测给定会话的用户参与度。系统模型包括一个数据处理流水线、作为数字孪生的机器学习模型以及一个用于预测参与度的统一模型。DIGITWISE在数字孪生和统一模型中均采用了XGBoost模型。所提出的架构证明了个人用户敏感性的重要性，与非用户感知模型相比，将用户参与度预测误差降低了高达5.8%。此外，DIGITWISE能够通过识别最大化参与度的特征来优化内容提供与传输，从而实现平均参与度提升高达8.6%。