Abstract Finding geospatial data has been a big challenge regarding the data size and heterogeneity across various domains. Previous work has explored using machine learning to improve geospatial data search ranking, but it usually relies on training data labelled by subject matter experts, which makes it laborious and costly to apply to scenarios in which data relevancy to a query can change over time. When a user interacts with a search engine, plenteous information is recorded in the log file, which is essentially free, sustainable and up-to-the-minute. In this research, we propose a deep learning-based search ranking framework that can expeditiously update the ranking model through capturing real-time user clickstream data. The contributions of the proposed framework consist of 1) a log parser that can ingest and parse Web logs that record users’ behavior in a real-time manner; 2) a set of hypotheses of modelling the relative relevance of data; and 3) a deep learning based ranking model which can be updated dynamically with the increment of user behavior data. Quantitative comparison with a few other machine learning algorithms suggests substantial improvement.

中文翻译：

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基于深度学习的用户行为数据提升地理空间数据搜索排名

摘要 查找地理空间数据一直是跨领域的数据大小和异构性的一大挑战。以前的工作探索了使用机器学习来提高地理空间数据搜索排名，但它通常依赖于由主题专家标记的训练数据，这使得应用于查询的数据相关性可能随时间变化的场景变得费力且成本高昂。当用户与搜索引擎交互时，日志文件中会记录大量信息，这些信息基本上是免费的、可持续的、最新的。在这项研究中，我们提出了一种基于深度学习的搜索排名框架，可以通过捕获实时用户点击流数据来快速更新排名模型。所提出框架的贡献包括：1) 一个日志解析器，可以实时摄取和解析记录用户行为的 Web 日志；2）对数据的相对相关性进行建模的一组假设；3）基于深度学习的排名模型，可以随着用户行为数据的增加而动态更新。与其他一些机器学习算法的定量比较表明有实质性的改进。