臺灣博碩士論文加值系統

隨著科技技術與網路發達，許多使用者在有室內裝潢的需求時都會先在網路上尋找相關資訊以及喜好的設計師。因此了解消費者喜好、更清晰地理解客人的樣貌變成是企業很重視的環節。本研究以室內裝潢平台為研究範圍。透過網路日誌可以記錄顧客旅程資料，其中包括了顧客瀏覽網頁的各種行為。本研究使用決策樹、支援向量機、文字探勘以及主成分分析方式分析顧客旅程資料。結果發現決策樹分類模型具有較良好之預測效能，也發現了此平台較重要的面相。透過分析顧客旅程資料，我們不僅可以識別出網站的潛在顧客，也可以了解到顧客所在意的面相，進而改善網路用戶的體驗，最終提升消費者在網路上購物的意願。

Many customers will search information or favorite designers on the Internet when they have the needs of interior decoration with the rapid development of the Internet network. Therefore, it becomes a very important part of the company that understanding the preferences of consumers. The purpose of the study was explored the customer journey form the interior decoration platform. The data of customer journey can be recorded through the web log, including various behaviors of the customer browsing the web. This study used the decision trees, support vector machines, text mining and principal component analysis to analyze the data of customer journey. Based on the classification technology, the decision trees have the best performance. We can not only identify potential customers of the website but also improve the experience of online users by analyzing data of customer journey, and lastly enhance the willingness of consumers to shop on the Internet.

摘要 ii
Abstract iii
目錄 iv
表目錄 vi
圖目錄 vii
1. 緒論 1
1.1　　研究動機與目的 1
1.2　　研究流程 2
2. 相關文獻 3
2.1　　顧客旅程(Customer Journey) 3
2.2　　網路日誌(Web Log) 3
2.3　　文字探勘(Text Mining) 4
2.4　　分類技術(Classification Techniques) 5
2.4.1　決策樹(Decision trees) 5
2.4.2　支援向量機(Support Vector Machine, SVM) 6
2.5　　主成分分析(Principal component analysis, PCA) 6
3. 研究方法 7
3.1　　資料預處理 8
3.1.1　網路日誌(Web log) 8
3.1.2　網頁文章 10
3.2　　資料篩選 13
3.3　　模型建立 13
3.4　　資料分析 13
4. 實驗與結果 15
4.1　　實驗資料 15
4.2　　實驗結果 15
4.2.1　資料預處理 15
4.2.2　資料篩選 18
4.2.3　模型建立 18
4.2.4　資料分析 22
5. 結論 24
5.1　　研究貢獻 24
5.2　　研究限制與未來發展方向 24
參考文獻 25

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