請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/21272完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張瑞益(Ray-I Chang) | |
| dc.contributor.author | Po-Yen Wu | en |
| dc.contributor.author | 吳伯彥 | zh_TW |
| dc.date.accessioned | 2021-06-08T03:29:59Z | - |
| dc.date.copyright | 2019-08-18 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-08-14 | |
| dc.identifier.citation | [1] Retail Ecommerce Sales Worldwide, 2016-2021 https://www.emarketer.com/Chart/Retail-Ecommerce-Sales-Worldwide-2016-2021-trillions-change-of-total-retail-sales/215138
[2] Nasraoui, O., Spiliopoulou, M., Srivastava, J., Mobasher, B., & Masand, B. (Eds.). (2007). Advances in Web Mining and Web Usage Analysis: 8th International Workshop on Knowledge Discovery on the Web, WebKDD 2006 Philadelphia, USA, August 20, 2006 Revised Papers (Vol. 4811). Springer. [3] Khan, W., & Shahzad, W. (2017). Predictive Performance Comparison Analysis of Relational & NoSQL Graph Databases. Int. J. Adv. Comput. Sci. Appl, 8, 523-530. [4] Langville, A. N., & Meyer, C. D. (2011). Google's PageRank and beyond: The science of search engine rankings. Princeton University Press. [5] Rhodes, C., Blewitt, W., Sharp, C., Ushaw, G., & Morgan, G. (2014, July). Smart routing: A novel application of collaborative path-finding to smart parking systems. In CBI (1) (pp. 119-126). [6] Liben‐Nowell, D., & Kleinberg, J. (2007). The link‐prediction problem for social networks. Journal of the American society for information science and technology, 58(7), 1019-1031. [7] Pelillo, M. (1999). Replicator equations, maximal cliques, and graph isomorphism. In Advances in Neural Information Processing Systems (pp. 550-556). [8] Lei, C., & Ruan, J. (2012). A novel link prediction algorithm for reconstructing protein–protein interaction networks by topological similarity. Bioinformatics, 29(3), 355-364. [9] Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V., Malinowski, M., ... & Gulcehre, C. (2018). Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261. [10] Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907. [11] Schlichtkrull, M., Kipf, T. N., Bloem, P., Van Den Berg, R., Titov, I., & Welling, M. (2018, June). Modeling relational data with graph convolutional networks. In European Semantic Web Conference (pp. 593-607). Springer, Cham. [12] Yang, B., Yih, W. T., He, X., Gao, J., & Deng, L. (2014). Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575. [13] Harb, E., Kapellari, P., Luong, S., & Spot, N. (2011). Responsive web design. Version of, 6. [14] Rodriguez-Burrel, J. (2009). Google Analytics: good and nice and free. Profesional de la Información, 18(1), 67e71. [15] Gupta, S., & Rawat, M. (2016, December). Recommendations through click stream: tracking the need, current work and future directions. In 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I) (pp. 736-740). IEEE. [16] Shaikh, S., Rathi, S., & Janrao, P. (2017, January). Recommendation system in E-commerce websites: A Graph Based Approached. In 2017 IEEE 7th International Advance Computing Conference (IACC) (pp. 931-934). IEEE. [17] Tim Berners-Lee. (2018). One Small Step for the Web. https://medium.com/@timberners_lee/one-small-step-for-the-web-87f92217d085 [18] Bakharia, A., Kitto, K., Pardo, A., Gašević, D., & Dawson, S. (2016, April). Recipe for success: lessons learn from using xAPI within the connected learning analytics toolkit. In Proceedings of the sixth international conference on learning analytics & knowledge (pp. 378-382). ACM. [19] Classroom Aid. (2014). Experience API (xAPI) 追蹤所有學習經驗 http://chinese.classroom-aid.com/2014/05/experience-api-xapi.html/ [20] Classroom Aid. (2014). SCORM 簡介 – Overview. http://chinese.classroom-aid.com/2014/01/scorm-overview-i.html/ [21] Zeng, J., Yang, L. T., Lin, M., Ning, H., & Ma, J. (2016). A survey: Cyber-physical-social systems and their system-level design methodology. Future Generation Computer Systems. [22] Consumer Psychology and The E commerce Checkout – Stats Behind The Clicks https://www.coxblue.com/consumer-psychology-and-the-e-commerce-checkout-stats-behind-the-clicks-infographic/ [23] 完全了解 A/B 測試 https://tinyurl.com/yy3awxrw [24] Neo4j Decreases Development Time-to-Market for LinkedIn’s Chitu App. https://neo4j.com/news/neo4j-decreases-development-time-market-linkedins-chitu-app/ [25] Paszcza, B. (2016). Comparison of Microsoft academic (graph) with web of science, scopus and google scholar (Doctoral dissertation, University of Southampton). [26] Gutfraind, A., & Genkin, M. (2017). A graph database framework for covert network analysis: An application to the Islamic State network in Europe. Social Networks, 51, 178-188. [27] Jessie Chuang (2016).Experience API (xAPI): Potential for Open Educational Resources. https://www.academia.edu/21946922/Experience_API_xAPI_Potential_for_Open_Educational_Resources [28] Relational Graph Convolutional Network Tutorial https://docs.dgl.ai/en/latest/tutorials/models/1_gnn/4_rgcn.html#r-gcn-a-brief-introduction [29] 秦暐峻. (2018). 改善網站維運管理之智慧情境感知管理系統. 臺灣大學工程科學及海洋工程學研究所學位論文, 1-42. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/21272 | - |
| dc.description.abstract | VoucherCloud的統計[22]顯示，有92.6%的消費者表示，好的網站頁面設計是影響購買決策的主要因素，使用者導向設計(User-Centered Design)已是目前電商網站設計的主流趨勢。為了不斷改善使用者體驗，電商網站透過蒐集分析工具追蹤使用者行為，分析使用者與商品的關係，網站維護者在網頁上架後根據分析結果調整介面布局、網站流程及程式效能等。然而由於使用者行為紀錄沒有標準化的格式，資料蒐集過程往往受限於單一平台，蒐集到的行為資料難以深化運用。因此，本研究以標準資料格式xAPI (eXperience Application Programming Interface)為基礎開發一個使用者行為的圖形分析系統。相對於現有以統計分析方法為主，且僅聚焦於使用者與產品之間關係的電商網站分析工具，我們透過轉換使用者行為資料為圖形資料，並運用圖形方法Pattern Matching、Graph Similarity及圖神經網路(Graph Neural Network)進行資料分析，可有效分析使用者與網站內容的互動關係，根據使用者瀏覽歷程進行個人化商品推薦、尋找產品購買關聯內容區塊，並預測內容之間的關聯關係。此分析結果可提供網站維護者維運管理時，網頁重構的建議，並調整線下營運安排，有效節省成本。我們以實際線上運作的網站log進行實驗，實驗結果顯示本系統可以有效縮短使用者在整個購買流程路徑以改善使用者經驗。 | zh_TW |
| dc.description.abstract | According to VoucherCloud's survey [22], nearly 93% of customers regard the good UI/UX design as the key factor of online shopping. User-Centered Design has become a mainstream trend of e-commerce website design. To improve the user experience, e-commerce websites track users’ behaviors. They analyze the relationship between users and products. Then the maintainer tunes the website’s layout, workflow, performance according to these analysis results. However, the behavior tracking and its data collection are often platform-dependent as the lack of the standard format for recording user’s behaviors. Moreover, conventional e-commerce web analysis tools usually focus on statistical analysis with the relationship between users and products. As user behavior data are graphical data, we develop a graphical analysis system for user behavior based on xAPI (eXperience Application Programming Interface) standard data format. In this paper, we convert xAPI user behavior data into graphical data for analyzing by graphical methods and graph neural network. We analyze the relationship between users and web contents for personal recommendation via user browsing history.
The proposed method can also find key contents of products and predict link between contents. These analysis results can provide suggestions for website reconfiguration in DevOps, and adjust the offline operation arrangement to save costs. We analyze real e-commerce web log data in the experiment, and the result shows our system can shorten the user’s shopping process effectively to improve user experience. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T03:29:59Z (GMT). No. of bitstreams: 1 ntu-108-R06525055-1.pdf: 4460669 bytes, checksum: 4f30344d5a3ff6b8d06be096b000c740 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 摘要 i
ABSTRACT ii 論文目錄 iv 圖目錄 vi 表目錄 viii 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機與目的 3 1.3 論文架構 6 第二章 文獻探討 7 2.1 圖形資料庫 7 2.2 圖形分析分法 9 2.3 xAPI相關研究與介紹 14 第三章 研究方法 17 3.1 資料前處理 17 3.2 個人化推薦 20 3.3 整體頁面布局改善推薦 21 第四章 實驗結果與討論 24 4.1 個人化推薦 24 4.2 銷量與頁面關聯分析 287 4.3 頁面高度過長問題 29 第五章 結論與未來展望 33 參考文獻 36 | |
| dc.language.iso | zh-TW | |
| dc.title | 以圖形分析及圖神經網路改善電商網站使用者經驗 | zh_TW |
| dc.title | Improving user experience in e-commerce website with graphical method and graph neural network | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 何建明(Jan-Ming Ho),張恆華(Herng-Hua Chang),尹邦嚴(Peng-Yeng Yin) | |
| dc.subject.keyword | 圖神經網路,圖形分析,xAPI,使用者行為,使用者經驗, | zh_TW |
| dc.subject.keyword | graph neural network,graph analysis,xAPI,user behavior,user experience, | en |
| dc.relation.page | 39 | |
| dc.identifier.doi | 10.6342/NTU201903083 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2019-08-15 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
| 顯示於系所單位： | 工程科學及海洋工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-108-1.pdf 目前未授權公開取用 | 4.36 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。