In this paper, the possibility of quantifying a person's interest using data-driven algorithms is investigated. In doing so, interest estimation problem is formulated as a latent state estimation problem, and an answer is deduced via Bayesian Inference. First, a Subjective-Objective approach is used to measure activity. Through this calculated activity, the method indirectly infers human latent state values. A formulation of interest is then presented by drawing inspiration from the Ornstein-Uhlenbeck (OU) process in Physics. Moreover, concepts of stochastic volatility are employed to vary the instantaneous volatility of the OU process. This is done to further improve the performance. Subsequently, the convergence speed of the OU process is varied with time. A novel statistical framework is discussed that dynamically transforms interest into activity. Each of these individual contributions is combined to present a solution via Monte Carlo Simulations. To demonstrate the efficacy of the proposed method, numerical simulations are performed on real datasets. Lastly, a prototype is engineered and the method is implemented as a RESTful Web service. The prototype is hosted as a Web service on several Virtual Machines to demonstrate the practical feasibility of the framework in cloud-based deployment scenarios.

中文翻译：

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预测人类兴趣的原型模型：基于数据的人机结合设计

在本文中，研究了使用数据驱动算法量化一个人的兴趣的可能性。这样做时，兴趣估计问题被表述为潜在状态估计问题，并通过贝叶斯推理推导出答案。首先，使用主客观方法来衡量活动。通过这个计算的活动，该方法间接推断人类潜在状态值。然后通过从物理学中的 Ornstein-Uhlenbeck (OU) 过程中汲取灵感来提出一个感兴趣的公式。此外，随机波动率的概念被用来改变 OU 过程的瞬时波动率。这样做是为了进一步提高性能。随后，OU 过程的收敛速度随时间而变化。讨论了一种将兴趣动态转换为活动的新颖统计框架。这些单独的贡献中的每一个都被结合起来，通过蒙特卡洛模拟提供一个解决方案。为了证明所提出方法的有效性，在真实数据集上进行了数值模拟。最后，设计了一个原型，并将该方法实现为 RESTful Web 服务。该原型作为 Web 服务托管在多个虚拟机上，以证明该框架在基于云的部署场景中的实际可行性。设计了一个原型，并将该方法实现为 RESTful Web 服务。该原型作为 Web 服务托管在多个虚拟机上，以证明该框架在基于云的部署场景中的实际可行性。设计了一个原型，并将该方法实现为 RESTful Web 服务。该原型作为 Web 服务托管在多个虚拟机上，以证明该框架在基于云的部署场景中的实际可行性。