用贝叶斯方法实时学习用户习惯，动态调整界面。
Robust and continuous machine learning of usage habits to adapt digital interfaces to user needs
- 基于贝叶斯统计建模个体浏览习惯，不依赖群体偏好。
- 支持在线增量学习，小数据下仍能稳定预测。
- 适合需要个性化自适应的数字界面系统开发者。
本文提出一种机器学习方法，用于设计可动态适应不同用户及使用策略的数字界面。该算法采用贝叶斯统计建模用户的浏览行为，聚焦于个体习惯而非群体偏好。其核心优势在于在线增量学习能力，可在数据稀少且环境变化的情况下仍保持可靠预测。该推断方法生成任务模型，以图形化方式呈现当前用户的导航路径与使用统计。算法在学习新任务的同时保留已有知识。理论框架已建立，仿真结果验证了其在平稳与非平稳环境下的有效性。本研究为提升用户体验提供了自适应系统的新路径，帮助用户更高效地操作界面。
原文摘要 · Abstract (English)
The paper presents a machine learning approach to design digital interfaces that can dynamically adapt to different users and usage strategies. The algorithm uses Bayesian statistics to model users' browsing behavior, focusing on their habits rather than group preferences. It is distinguished by its online incremental learning, allowing reliable predictions even with little data and in the case of a changing environment. This inference method generates a task model, providing a graphical representation of navigation with the usage statistics of the current user. The algorithm learns new tasks while preserving prior knowledge. The theoretical framework is described, and simulations show the effectiveness of the approach in stationary and non-stationary environments. In conclusion, this research paves the way for adaptive systems that improve the user experience by helping them to better navigate and act on their interface.
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