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A method for discovering clusters of e-commerce interest patterns using click-stream data

机译：一种使用点击流数据发现电子商务兴趣模式集群的方法

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摘要

Having a good understanding of users' interests has become increasingly important for online retailers hoping to create a personalized service for a target market. Generally speaking, user's browsing behaviors (when looking at websites) represent a comprehensive reflection of their interests. Users with various interests will visit multiple categories and research various items. Their browsing paths, the frequency of page visits and the time spent on each category all vary widely. Based on these considerations, a novel approach to discovering consumers' interests is proposed and is systematically studied in this paper. The browsing behavior of a number of consumers - including their visiting sequence, frequency and time spent on each category - are mined via the click-stream data recorded on an e-commerce website. Given this behavioral data, we construct an improved leader clustering algorithm and leverage it with a rough set theory in order to generate users' interest patterns. Furthermore, a case study is conducted based on nearly three million click-stream data, which was collected from one of the largest Chinese e-commerce websites. Using this data, the parameters of the algorithm are tested and optimized to make the algorithm more effective in terms of large data analysis and to make it more suitable for discovering users' multiple interests. Using this algorithm, three typical user interest patterns are derived based on a real click-stream dataset. More importantly, further calculations based on different click-stream datasets verify that these three interest patterns are consistent and stable. This study demonstrates that the proposed algorithm and the derived interest patterns can provide significant assistances on webpage optimization and personalized recommendation. (C) 2014 Elsevier B.V. All rights reserved.
机译：对于希望为目标市场创建个性化服务的在线零售商而言，充分了解用户的兴趣已变得越来越重要。一般而言，用户的浏览行为（在查看网站时）代表了其兴趣的全面反映。兴趣各异的用户将访问多个类别并研究各种项目。它们的浏览路径，页面访问频率以及在每个类别上花费的时间都相差很大。基于这些考虑，提出了一种发现消费者兴趣的新颖方法，并对其进行了系统的研究。通过记录在电子商务网站上的点击流数据，可以挖掘出许多消费者的浏览行为，包括他们的访问顺序，频率和在每个类别上花费的时间。在此行为数据的基础上，我们构建了一种改进的领导者聚类算法，并通过粗糙集理论加以利用，以生成用户的兴趣模式。此外，基于近300万点击流数据进行了案例研究，这些数据是从中国最大的电子商务网站之一收集的。使用这些数据，对算法的参数进行测试和优化，以使该算法在大数据分析方面更加有效，使其更适合发现用户的多种兴趣。使用该算法，可以基于真实的点击流数据集得出三种典型的用户兴趣模式。更重要的是，基于不同点击流数据集的进一步计算证明了这三种兴趣模式是一致且稳定的。这项研究表明，所提出的算法和导出的兴趣模式可以为网页优化和个性化推荐提供重要的帮助。 （C）2014 Elsevier B.V.保留所有权利。

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