在網路快速發展的時代網路購物日漸普及，電子商務網站記錄中不斷累積使用者相關紀錄，如何有效運用大量資料成為重要的課題。過去有相關研究運用網站上的使用者瀏覽紀錄進行研究，有學者透過流程探勘分析使用者瀏覽軌跡，也有研究使用機器學習方法瀏覽階段的購買與否，而資料變數可由瀏覽紀錄生成，但以往的研究使用到實際紀錄並不多，即使為實際瀏覽資料也沒有太多的樣本。本研究資料來自台灣一家線上零售平台提供為期三個月的使用者於網站的瀏覽紀錄，本研究目標為剖析使用者瀏覽行為模式並建立相關預測模型，我們透過分析不同客群瀏覽特性、使用流程探勘工具診斷瀏覽過程、運用機器學習預測購買結果，最後整合上述結果回饋有價值的資訊給線上零售平台業者。

The purpose of this research is trying to find out valuable information by figuring out user browsing process and predicting user behavior. In this study, a dataset provided by an online retailer in Taiwan contains three months click-stream information. We analyze the browsing behavior features of different age and gender, apply a process mining tool to diagnose user session, establish machine learning model to predict users' future behaviors in terms of whether they will buy or not. Consequently, we present the valuable integrated outcome to manager as feedback.

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