政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/153376

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/153376

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文笔数/总笔数 : 112704/143671 (78%)
造访人次 : 49770532 在线人数 : 667

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

搜寻范围

查询小技巧：

您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果

若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据

进阶搜寻

主页 ‧ 登入 ‧ 上传 ‧ 说明 ‧ 关于政大典藏 ‧ 管理

到手机版

政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 > Item 140.119/153376

请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/153376

题名:	運用知識圖譜由交易資料預測人口統計資料 Demographic Prediction from Transaction Data Using Knowledge Graph
作者:	巫謹任 Wu, Jin-Ren
贡献者:	沈錳坤 Shan, Man-Kwan 巫謹任 Wu, Jin-Ren
关键词:	人口統計預測知識圖 Demographic Prediction Knowledge Graph
日期:	2024
上传时间:	2024-09-04 14:59:21 (UTC+8)
摘要:	隨著大數據時代的興起，人們在網路上所提供的資訊擁有巨大的價值。對多數企業而言，用戶的背景資料是極為寶貴的，能助企業更精確地制定策略並提供個人化的服務。然而，隨著人們對隱私的日益重視，許多人選擇不在網路上公開自己的背景資料。為了解決這個問題，許多學者嘗試從用戶的互動交易資料(User-Item transaction data)中預測用戶的背景標籤資料（即人口統計資料）。本研究試圖從另一個角度切入，在預測結果的同時也提供模型的可解釋性，從而增加預測的可靠性和使用者的信賴度。本研究旨在通過知識圖譜(Knowledge Graph)，來預測用戶的人口統計資料，並結合深度學習技術以增強預測的準確性和可解釋性。本論文由知識圖譜與互動交易資料所形成的二分圖中，產生用戶結點到人口統計節點的路徑，透過包含語意的嵌入向量轉換後，經過長短期記憶模型學習路徑中的前後關係，最後透過加權池化層來預測人口統計資料。 With the rise of the big data era, the information provided by individuals online possesses immense value. For most businesses, users' background data is highly valuable as it helps them formulate strategies more precisely and offer personalized services. However, as people become increasingly concerned about privacy, many choose not to disclose their background information online. To address this issue, many researchers have attempted to predict users' background labels (i.e., demographic data) from User-Item transaction data. This study approaches the problem from a different angle, aiming to enhance the reliability of predictions and build user trust by providing model interpretability alongside the predictions. The research aims to predict users' demographic data using a Knowledge Graph while integrating deep learning techniques to improve both the accuracy and interpretability of the predictions. In this thesis, paths from user nodes to demographic nodes are generated from the knowledge graph integrated with the bipartite graph formed from transaction data. After transforming the paths into semantically rich embedding vectors, a Long Short-Term Memory (LSTM) model is employed to learn the sequential relationships within the paths. Finally, a weighted pooling layer is used to predict the demographic data.
參考文獻:	[1] U. Weinsberg, S. Bhagat, S. Ioannidis, and N. Taft, “BlurMe: Inferring and Obfuscating User Gender Based on Ratings,” in Proceedings of the Sixth ACM Conference on Recommender Systems, 2012. [2] P. Wang, J. Guo, Y. Lan, J. Xu, and X. Cheng, “Your Cart Tells You: Inferring Demographic Attributes from Purchase Data,” in Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, 2016. [3] A. Mislove, B. Viswanath, K. P. Gummadi, and P. Druschel, “You Are Who You Know: Inferring User Profiles in Online Social Networks,” in Proceedings of the third ACM International Conference on Web Search and Data Mining, 2010. [4] X. Wang, D. Wang, C. Xu, X. He, Y. Cao, and T.-S. Chua, “Explainable Reasoning over Knowledge Graphs for Recommendation,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, 2019. [5] S. Bhagat, I. Rozenbaum, and G. Cormode, “Applying Link-based Classification to Label Blogs,” in Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web Mining and Social Network Analysis, 2007. [6] D. Rao, D. Yarowsky, A. Shreevats, and M. Gupta, “Classifying Latent User Attributes in Twitter,” in Proceedings of the 2nd International Workshop on Search and Mining User-Generated Contents, 2010. [7] J. Otterbacher, “Inferring Gender of Movie Reviewers: Exploiting Writing Style, Content and Metadata,” in Proceedings of the 19th ACM International Conference on Information and Knowledge Management, 2010. [8] J. Hu, H.-J. Zeng, H. Li, C. Niu, and Z. Chen, “Demographic Prediction Based on User’s Browsing Behavior,” in Proceedings of the 16th International Conference on World Wide Web, 2007. [9] E. Zhong, B. Tan, K. Mo, and Q. Yang, “User Demographics Prediction Based on Mobile Data,” Pervasive and Mobile Computing, vol. 9, no. 6, 2013. [10] A. Culotta, N. Kumar, and J. Cutler, “Predicting the Demographics of Twitter Users from Website Traffic Data,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29, no. 1, 2015. [11] C.-T. Lai, C.-T. Li, and S.-D. Lin, “Deep Energy Factorization Model for Demographic Prediction,” ACM Trans. Intell. Syst. Technol., vol. 12, no. 1, 2020. [12] A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko, “Translating Embeddings for Modeling Multi-relational Data,” in Advances in Neural Information Processing Systems, 2013. [13] Y. Lin, Z. Liu, M. Sun, Y. Liu, and X. Zhu, “Learning Entity and Relation Embeddings for Knowledge Graph Completion,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29, no. 1, 2015. [14] Z. Wang, J. Zhang, J. Feng, and Z. Chen, “Knowledge Graph Embedding by Translating on Hyperplanes,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28, no. 1, 2014. [15] Y. Jiang, W. Tang, N. Gao, J. Xiang, and Y. Su, “Demographic Prediction from Purchase Data Based on Knowledge-Aware Embedding,” in Neural Information Processing: 26th International Conference, ICONIP, 2019. [16] S. Chaudhari, A. Azaria, and T. Mitchell, “An Entity Graph Based Recommender System,” AI Communications, vol. 30, no. 2, 2017. [17] Y. Sun, J. Han, X. Yan, P. S. Yu, and T. Wu, “Pathsim: Meta Path-based Top-K Similarity Search in Heterogeneous Information Networks,” Proceedings of the VLDB Endowment, 2011.
描述:	碩士國立政治大學資訊科學系 111753124
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0111753124
数据类型:	thesis
显示于类别:	[資訊科學系] 學位論文

文件中的档案:

档案	大小	格式	浏览次数
312401.pdf	1488Kb	Adobe PDF	0	检视/开启

在政大典藏中所有的数据项都受到原著作权保护.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - 回馈