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首页> 外文期刊>Journal of Uncertainty Analysis and Applications >Predicting Human Interest: An Application of Artificial Intelligence and Uncertainty Quantification
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Predicting Human Interest: An Application of Artificial Intelligence and Uncertainty Quantification

机译：预测人类利益：人工智能和不确定性量化的应用

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摘要

The idea that a machine can numerically estimate the interest of an individual towards any entity (e.g., WhatsApp, Facebook) is fascinating. Interest, however, is a complex human property that cannot be quantified by another person; to have a machine-driven method quantify this unobservable and intangible internal property is challenging. In this paper, we make an attempt to address this issue. We propose a novel approach to estimate this internal state of a human. We formulate the interest prediction problem as a hidden state estimation problem and deduce a solution through Bayesian inference. In doing so, we apply indirect inference rules to estimate interest from activity. Activity as a consequence of interest is computed via a subjective-objective weighted approach. We further propose a model for interest by taking inspiration from physics. We use mean reverting stochastic procedures to capture the long-term dynamics of interest. With this perspective, a solution is provided via Monte Carlo simulations. To demonstrate the feasibility of the framework, we develop a web-based prototype and experiment with real-world datasets.
机译：机器可以从数字上估计个人对任何实体（例如WhatsApp，Facebook）的兴趣的想法令人着迷。然而，兴趣是复杂的人类财产，无法被他人量化。用机器驱动的方法来量化这种不可观察和无形的内部特性是具有挑战性的。在本文中，我们尝试解决此问题。我们提出了一种新颖的方法来估计人的这种内部状态。我们将兴趣预测问题表述为隐藏状态估计问题，并通过贝叶斯推理得出解决方案。为此，我们应用间接推理规则来估计活动的兴趣。通过主观-客观加权方法来计算作为兴趣结果的活动。我们进一步从物理学的启发中提出了一个感兴趣的模型。我们使用均值回复随机程序来捕获感兴趣的长期动态。从这个角度出发，通过蒙特卡洛模拟提供了一种解决方案。为了证明该框架的可行性，我们开发了一个基于Web的原型并使用实际数据集进行了实验。

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