论文标题
使用应用程序的点击流数据进行客户行为分析的基于图的平台
A Graph-Based Platform for Customer Behavior Analysis using Applications' Clickstream Data
论文作者
论文摘要
自电子商务和应用程序中使用增加以来，ClickStream分析引起了人们的关注。除了客户的购买行为分析外，还尝试分析与Web或应用程序设计质量有关的客户行为。通常，ClickStream数据可以视为在不同级别的Web/App用法中收集的日志事件序列。可以直接作为序列分析或通过从序列提取特征来直接执行点击流数据的分析。在这项工作中，我们展示了如何用其基础图结构来代表和保存序列可以诱导一个用于客户行为分析的平台。我们的主要思想是，ClickStream数据包含应用程序的动作序列，是该应用程序的相应有限状态自动机（FSA）的步行。我们的假设是，应用程序的客户通常不会在FSA中使用所有可能的步行，而实际步行的数量比通过FSA的可能步行的总数小得多。这样的步行序列通常由FSA图上的有限数量的周期组成。在经典序列分析中识别和匹配这些周期不是直接的。我们显示，通过其基础图结构表示序列不仅会自动分组序列，还提供了原始序列的压缩数据表示。
Clickstream analysis is getting more attention since the increase of usage in e-commerce and applications. Beside customers' purchase behavior analysis, there is also attempt to analyze the customer behavior in relation to the quality of web or application design. In general, clickstream data can be considered as a sequence of log events collected at different levels of web/app usage. The analysis of clickstream data can be performed directly as sequence analysis or by extracting features from sequences. In this work, we show how representing and saving the sequences with their underlying graph structures can induce a platform for customer behavior analysis. Our main idea is that clickstream data containing sequences of actions of an application, are walks of the corresponding finite state automaton (FSA) of that application. Our hypothesis is that the customers of an application normally do not use all possible walks through that FSA and the number of actual walks is much smaller than total number of possible walks through the FSA. Sequences of such a walk normally consist of a finite number of cycles on FSA graphs. Identifying and matching these cycles in the classical sequence analysis is not straight forward. We show that representing the sequences through their underlying graph structures not only groups the sequences automatically but also provides a compressed data representation of the original sequences.
