用社交数据建模游客行为，预测下一站去哪。
Hidden markov model to predict tourists visited place
- 基于社交网络数据，用语法推断算法构建隐马尔可夫模型
- 在巴黎数据上验证，能有效捕捉游客移动规律
- 模型可动态更新，适合实时旅游决策支持
如今，社交媒体成为分析游客行为的热门途径，因为旅行者在行程中留下的数字足迹（如评论、照片）产生了海量数据。这些数据使我们能够建模旅程并分析行为模式。预测游客下一步目的地对旅游营销至关重要，有助于理解需求并优化决策支持。本文提出一种方法，通过分析社交网络数据来理解并学习游客移动规律，进而预测未来动向。该方法基于机器学习语法推断算法，主要贡献在于将其适配于大数据场景。所提方法生成一个代表一组游客移动的隐马尔可夫模型，具备灵活性与可编辑性，可随新数据持续更新。以法国首都巴黎为例，验证了该方法的有效性。
原文摘要 · Abstract (English)
Nowadays, social networks are becoming a popular way of analyzing tourist behavior, thanks to the digital traces left by travelers during their stays on these networks. The massive amount of data generated; by the propensity of tourists to share comments and photos during their trip; makes it possible to model their journeys and analyze their behavior. Predicting the next movement of tourists plays a key role in tourism marketing to understand demand and improve decision support. In this paper, we propose a method to understand and to learn tourists' movements based on social network data analysis to predict future movements. The method relies on a machine learning grammatical inference algorithm. A major contribution in this paper is to adapt the grammatical inference algorithm to the context of big data. Our method produces a hidden Markov model representing the movements of a group of tourists. The hidden Markov model is flexible and editable with new data. The capital city of France, Paris is selected to demonstrate the efficiency of the proposed methodology.
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