政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/131066

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/131066

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文笔数/总笔数 : 112721/143689 (78%)
造访人次 : 49511672 在线人数 : 792

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 > 文學院 > 圖書資訊與檔案學研究所 > 學位論文 > Item 140.119/131066

请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/131066

题名:	建構圖書館個人化推薦系統-以國立政治大學為例 Personalized Books Recommendation System: Case Study of National Chengchi University
作者:	林芳均 Lin, Fang-Chun
贡献者:	陳志銘 Chen, Chih-Ming 林芳均 Lin,Fang-Chun
关键词:	推薦系統數位圖書館以內容為基礎演算法以協同過濾為基礎演算法矩陣分解法資訊視覺化 Recommender System Digital Library Content-Based Filtering Collaborative Filtering Matrix Factorization Data Visualization
日期:	2020
上传时间:	2020-08-03 17:49:28 (UTC+8)
摘要:	近年來由於資訊的快速增長，圖書館普遍面臨圖書借閱率下降的問題，而讀者如何從不斷擴增書籍的圖書館中找到符合自己喜歡的書籍也是一大考驗。因此，本研究應用政大新啟用之圖書館服務平台Ａlma所記錄之讀者圖書借閱記錄，並以圖書資訊學資訊組織角度，將個人及整體的圖書借閱紀錄應用於發展以內容為基礎及以協同過濾為基礎的個人化圖書推薦系統，利用主動且個人化的方式為讀者提供圖書推薦服務。本研究的推薦結果分為兩大功能呈現：「當月新書推薦」、「您可能感興趣推薦（舊書推薦）」，並予以「借閱記錄視覺化」輔助讀者了解個人借書行為習慣。研究結果顯示，本研究所發展的圖書館個人化書籍推薦系統功能中，以內容為基礎的推薦書籍多為符合個人閱讀偏好興趣的圖書，能加深讀者對於感興趣領域圖書的見聞;以協同過濾推薦之書籍則以多元化的閱讀類型推薦，可擴展讀者的閱讀視野。此外，本研究所發展的圖書館個人化書籍推薦系統可有效提升讀者前往圖書館借閱書籍的動力及增進其對於圖書館新書資訊的掌握，對於讀者及圖書館皆呈現正向的影響。再則個人借閱記錄視覺化的部分則可有效幫助讀者了解自己的閱讀行為及喜好脈絡。未來，個人化書籍推薦系統若能加入借閱時間作為推薦考量，將能使系統更加完善，更能滿足讀者當下的閱讀喜好。 Due to the rapid growth of information in recent years, libraries commonly face the problem of declining book borrowing rates, as readers find it difficult to find the book they need from an ever-increasing number of books. Hence, this study applied the book loan data from the library service platform Alma, which was newly launched by National Chengchi University Library, to record the borrowing records of readers. Taking the perspectives in Cataloging and Classification of Library and Information Science, this research was conducted by applying library loan records to two kinds of algorithm, based on content-based filtering (CBF) and collaborative filtering (CF). The results were shown via the functions “The Month New Arrival Books” and “You Maybe Like” on the interface. In addition, the system included “Visualized Borrowing Records” to help readers know their borrowing habits. The experimental results show that within the system’s functions, content-based filtering (CBF) recommended books were able to meet individual reading preferences, deepen readers’ insights into the topics of interest, while collaborative filtering (CF) recommended books across a wider spectrum, expanding readers’ horizons. Moreover, the result of this research confirmed that the library personalized recommender system effectively encouraged readers’ motivation to borrow books from the library, while enhancing readers’ understanding of the new books. This generated a positive impact on both the readers and the library. Furthermore, the visual part of personal borrowing records can help readers understand their reading behavior and preferences effectively. In the future, if the personalized book recommendation system can factor the length of time of each loan into consideration, it will further complete the system and meet the readers’ preferences.
參考文獻:	中文文獻戴玉旻 (2001)。圖書館借閱紀錄探勘系統。國立交通大學資訊科學與工程研究所碩士論文。蔡明月 (2018)。從ALMA與WMS系統評析探討雲端圖書館服務平台之發展與趨勢。檢自：https://nclfile.ncl.edu.tw/files/201903/b9192699-836b-4c51-80b5-8f10f709f2f0.pdf 余明哲 (2013)。圖書館個人化館藏推薦系統。國立交通大學資訊科學與工程研究所碩士論文。黃俊龍 (2010)。Bookle ：書籍推薦系統 - 基於讀者的評論與閱讀行為。資訊學院資訊科技(IT)產業研發碩士專班。郭俊桔、張瑞珊、張育蓉（2013）。導入矩陣分群之視覺化圖書推薦系統。教育資料與圖書館學，51(1)，4-35。doi:10.6120/JoEMLS.2013.511/0560.RS.AM 黃河銓( 2009 )。書籍社群推薦系統之建置。he 2009 Conference on Computer Science and Information Engineering Applications 。檢自：http://ir.lib.ypu.edu.tw/bitstream/310904600Q/8782/2/44.pdf 林正賢（2013）。結合社會性標籤與文獻內容於個人化學術文章推薦。國立中正大學資訊管理學系暨研究所碩士論文，嘉義縣。取自https://hdl.handle.net/11296/3brp72 陳灯能、蘇柏銘(2015)，結合腦波分析與內容導向過濾為基礎的文章推薦系統，中華民國資訊管理學報，第二十二卷，第二期，頁 141-170。英文文獻 Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering, 17(6), 734-749. Aditya, P. H., Budi, I., & Munajat, Q. (2016, October). A comparative analysis of memory-based and model-based collaborative filtering on the implementation of recommender system for E-commerce in Indonesia: A case study PT X. In 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS) (pp. 303-308). IEEE. Aggarwal, C. C. (2016). An Introduction to Recommender Systems. In Recommender Systems: The Textbook (pp. 1–28). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-29659-3_1 A. Kent et al (1979), “Use of Library Materials: The University of Pittsburgh Study,” PA.: Pittsburgh University. Retrievedfrom https://crl.acrl.org/index.php/crl/article/view/13418/14864 Aljukhadar Muhammad (2012), “Information Overload and Usage of Recommendations,”Retrievedfrom https://pdfs.semanticscholar.org/c4c4/c19e4887ecba253f9f21fbe63fdfff145cdd.pdf?_ga=2.112691779.654016936.1576730492-1021708040.1548091742 Alharthi, H., Inkpen, D., & Szpakowicz, S. (2017). A survey of book recommender systems. Journal of Intelligent Information Systems, 1-22. Bhat, W. A. (2018). Long-term preservation of big data: prospects of current storage technologies in digital libraries. Library Hi Tech, 36(3), 539–555. doi: 10.1108/lht-06-2017-0117 Breeding,M.(2013).BeyondtheILS:ANewGenerationofLibraryServicesPlatforms.InE. Iglesias(Ed.), Robots in Academic Libraries: Advancements in Library Automation. Hershey, Pa.: IGI Global. Becker, S. A., Cummins, M., Davis, A., Freeman, A., Giesinger, C. H., Ananthanarayanan, V., ... & Wolfson, N. (2017). NMC horizon report: 2017 library edition. The New Media Consortium Burke, Robin. (2002). Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction. 12. 10.1023/A:1021240730564. Burke, R. (2007). Hybrid Web Recommender Systems. In P.Brusilovsky, A.Kobsa, &W.Nejdl (Eds.), The Adaptive Web: Methods and Strategies of Web Personalization (pp. 377–408). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-72079-9_12 Fang, D., Yang, H., Gao, B. and Li, X. (2018), “Discovering research topics from library electronic references using latent Dirichlet allocation”, Library Hi Tech, Vol. 36 No. 3, pp. 400-410. Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), 61–70. doi: 10.1145/138859.138867 Gasper, Huba. (2015).The cold star problem for recommender systems. Retrived from https://www.yuspify.com/blog/cold-start-problem-recommender-systems/ Guan, X., Li, C. T., & Guan., Y. (2017). Matrix Factorization with Rating Completion: an Enhanced SVD Model for Collaborative Filtering Recommender Systems. IEEE Access, 5, 27668 - 27678. [27668]. https://doi.org/10.1109/ACCESS.2017.2772226 Huang, Z., Chung, W., &Chen, H. (2004). A Graph Model for E-commerce Recommender Systems. Journal of the American Society for Information Science and Technology, 55(3), 259–274. https://doi.org/10.1002/asi.10372 Josef Fink.(2004) User Modeling Servers: Requirements, Design, and Evaluation (Dissertations in Artificial Intelligence: Infix). Konstan, Joseph & Miller, Bradley & Maltz, David & Herlocker, Jon & Gordon, Lee & Riedl, John. (2000). GroupLens: Applying collaborative filtering to Usenet news. Communications of the ACM. 40. 10.1145/245108.245126. Koren, Y. (2008, August). Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 426-434). Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30-37. Lards, Binau (2017). Libraries at the crossroad. OCLC EMEA Regional Council Meeting.Retrived from https://www.oclc.org/content/dam/oclc/events/2017/EMEARC2017/EMEARC-2017-Session-L-Academic-Library-Transformation-Part-2-Lars-Binau.pdf Linden, G. D., Jacobi, J. A., & Benson, E. A. (2001). U.S. Patent No. 6,266,649. Washington, DC: U.S. Patent and Trademark Of ce. Linden, G., Smith, B., & York, J. (2003). Amazon. com recommendations: Item-to-item collaborative ltering. IEEE Internet computing, 7(1), 76-80. M. Fatemi and L. Tokarchuk, "A Community Based Social Recommender System for Individuals & Groups," 2013 International Conference on Social Computing, Alexandria, VA, 2013, pp. 351-356, doi: 10.1109/SocialCom.2013.55. Mehta, R., & Rana, K. (2017). A review on matrix factorization techniques in recommender systems. 2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA). doi:10.1109/cscita.2017.8066567 Miller, B. N., Konstan, J. A., & Riedl, J. (2004). PocketLens: Toward a personal recommender system. ACM Transactions on Information Systems (TOIS), 22(3), 437-476. Rendle, S. (2010, December). Factorization machines. In 2010 IEEE International Conference on Data Mining (pp. 995-1000). IEEE. Ricci, F., Rokach, L., &Shapira, B. (2015). Recommender Systems: Introduction and Challenges. In F.Ricci, L.Rokach, &B.Shapira (Eds.), Recommender Systems Handbook (pp. 1–34). Boston, MA: Springer US. https://doi.org/10.1007/978-1- 4899-7637-6_1. Nicholson, S.W. and Bennett, T.B. (2016), “Dissemination and discovery of diverse data: do libraries promote their unique research data collections?”, International Information & Library Review, Vol. 48 No. 2, pp. 85-93. Simović, A. (2018), “A Big Data smart library recommender system for an educational institution”, Library Hi Tech, Vol. 36 No. 3, pp. 498-523. Tejeda-Lorente, Á., Bernabé-Moreno, J., Porcel, C., Galindo-Moreno. (2015). A Dynamic Recommender System as Reinforcement for Personalized Education by a Fuzzly Linguistic Web System. Procedia Computer Science, 55, 1143–1150. doi: 10.1016/j.procs.2015.07.084 Tsuji, K., Kuroo, E., Sato, S., Ikeuchi, U., Ikeuchi, A., Yoshikane, F., & Itsumura, H. (2012). Use of Library Loan Records for Book Recommendation. 2012 IIAI International Conference on Advanced Applied Informatics. doi: 10.1109/iiai-aai.2012.16 Wang, J., De Vries, A. P., & Reinders, M. J. (2006, August). Unifying user- based and item-based collaborative filtering approaches by similarity fusion. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 501-508). ACM. Zhu, Q., Wu, Y., Li, Y., Han, J. and Zhou, X. (2018), “Text mining-based theme logic structure identification: application in library journals”, Library Hi Tech, Vol. 36 No. 3, pp. 411-425.
描述:	碩士國立政治大學圖書資訊與檔案學研究所 105155007
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0105155007
数据类型:	thesis
DOI:	10.6814/NCCU202001025
显示于类别:	[圖書資訊與檔案學研究所] 學位論文

文件中的档案:

档案	描述	大小	格式	浏览次数
500701.pdf		3326Kb	Adobe PDF2	69	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