請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/41033
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 許永真(Yung-jen Hsu) | |
dc.contributor.author | Yu-Chung Shen | en |
dc.contributor.author | 沈育仲 | zh_TW |
dc.date.accessioned | 2021-06-14T17:12:58Z | - |
dc.date.available | 2008-09-02 | |
dc.date.copyright | 2008-09-02 | |
dc.date.issued | 2008 | |
dc.date.submitted | 2008-07-25 | |
dc.identifier.citation | [1] Toward the next generation of recommender systems: A survey of the state-
of-the-art and possible extensions. IEEE Trans. on Knowl. and Data Eng., 17(6):734-749, 2005. Gediminas Adomavicius and Alexander Tuzhilin. [2] H. Ahn. Utilizing popularity characteristics for product recommendation. Int. J. Electron. Commerce, 11(2):59-80, 06-7. [3] K. Ali and W. V. Stam. Tivo: making show recommendations using a dis- tributed collaborative filtering architecture. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, 2004. [4] M Anderson, M. Ball, H. Boley, S. Greene, N. Howse, D. Lemire, and S. Mc- Grath. Racofi: A rule-applying collaborative filtering system. In Proceedings of COLA'03, 2003. [5] F. A. Asnicar and C. Tasso. ifweb: a prototype of user model-based intelligent agent for document filtering and navigation in the world wide web. In UM97 Workshop on 'Adaptive Systems and User Modeling on the World Wide Web' Sixth International Conference on User Modeling, 1997. [6] M. Balabanovi and Y. Shoham. Fab: content-based, collaborative recommen- dation. Commun. ACM, 40:66-72, 1997. [7] A. Birukov, E. Blanzieri, and P. Giorgini. Implicit: an agent-based recom- mendation system for web search. In AAMAS '05: Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems, pages 618-624, New York, NY, USA, 2005. ACM. [8] M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes, and M. Sartin. Combining content-based and collaborative filters in an online newspaper. In Proceedings of ACM SIGIR Workshop on Recommender Systems, 1999. [9] Y. Ding, X. Li, and M.E. Orlowska. Recency-based collaborative filtering. In Proceedings of Seventeenth Australasian Database Conference (ADC2006), 2006. [10] C.L. Giles, K.D. Bollacker, and S. Lawrence. Citeseer: an automatic citation indexing system. Proceedings of the third ACM conference on Digital libraries, pages 89-98, 1998. [11] D. Goldberg, D. Nichols, B. M. Oki, and D. Terry. Using collaborative ‾ltering to weave an information tapestry. Commun. ACM, 35:61-70, 1992. [12] F. M. Harper, S. Sen, and D. Frankowski. Supporting social recommendations with activity-balanced clustering. In RecSys '07: Proceedings of the 2007 ACM conference on Recommender systems, pages 165-168, New York, NY, USA, 2007. ACM. [13] J. L. Herlocker, J. A. Konstan, and J. Riedl. Explaining collaborative filtering recommendations. In Computer Supported Cooperative Work, 2000. [14] A. Hotho, R. Jaschke, C. Schmitz, and G. Stumme. Bibsonomy: A social book- mark and publication sharing system. Proc. of the Conceptual Structures Tool Interoperability Workshop at the 14th Int. Conf. on Conceptual Structures, Aal- borg, Denmark, July, 2006. [15] G. Karypis. Evaluation of item-based top-n recommendation algorithms. Tech- nical report, CS-TR-00-46, Computer Science Dept., University of Minnesota., 2000. [16] B. Krulwich. Lifestyle finnder: Intelligent user profiling using large-scale demo- graphic data. AI Magazine, 18:37{45, 1997. [17] K. Lang. NewsWeeder: learning to filter netnews. In Proceedings of the 12th International Conference on Machine Learning. Morgan Kaufmann publishers Inc.: San Mateo, CA, USA, 1995. [18] H. Lieberman, N. van Dyke, and A. Vivacqua. Let's browse: a collaborative web browsing agent. In USENIX Winter 1995 Technical Conference, 1999. [19] G. Linden, B. Smith, and J. York. Amazon.com recommendations: Item-to- item collaborative filtering. In IEEE Computer Society, volume 07, pages 76{80. IEEE Computer Society, 2003. [20] H. Liu and P. Maes. Interestmap: Harvesting social network profiles for rec- ommendations. In IUI Beyond Personalization 2005: A Workshop on the Next Stage of Recommender Systems Research, 2005. [21] A. Mathes. Folksonomies-cooperative classification and communication through shared metadata. Computer Mediated Communication, LIS590CMC (Doctoral Seminar), Graduate School of Library and Information Science, University of Illinois Urbana-Champaign, December, 2004. [22] S. M. McNee, J. Riedl, and J. Konstan. Making recommendations better: An analytic model for human-recommender interaction. In In the Extended Ab- stracts of the 2006 ACM Conference on Human Factors in Computing Systems (CHI 2006), April 2006. [23] Sean M. McNee, Istvan Albert, Dan Cosley, Prateep Gopalkrishnan, Shyong K. Lam, Al Mamunur Rashid, Joseph A. Konstan, and John Riedl. On the recommending of citations for research papers. In CSCW '02: Proceedings of the 2002 ACM conference on Computer supported cooperative work, pages 116-125, New York, NY, USA, 2002. ACM. [24] B. N. Miller, J. A. Konstan, and J. Riedl. Pocketlens: Toward a personal recommender system. ACM Trans. Inf. Syst., 22:437-476, 2004. [25] J. Pouwelse, M. V. Slobbe, J. Wang, M. J. T. Reinders, and H. Sips. P2p- based pvr recommendation using friends, taste buddies and superpeers. In Proceedings of Beyond Personalizaion 2005, the Workshop on the Next Sage of Recommender Systems Research (IUI2005), 2005. [26] P. Resnick, N. Iacovou, M. Suchak, P. Bergstorm, and J. Riedl. Grouplens: An open architecture for collaborative ‾ltering of netnews. In Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work, pages 175-186. ACM, 1994. [27] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative ‾l- tering recommendation algorithms. In Proceedings of the 10th International World Wide Web Conference (WWW10), 2001. [28] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Recommender systems for large-scale e-commerce: Scalable neighborhood formation using clustering. In Proceedings of the Fifth International Conference on Computer and Information Technology (ICCIT 2002), 2002. [29] J. B. Schafer, J. A. Konstan, and J. Riedl. E-commerce recommendation appli- cations. Data Mining and Knowledge Discovery, 5:115-153, 2001. [30] U. Shardanand and P. Maes. Social information filtering: Algorithms for au- tomating word of mouth'. In Proceedings of ACM CHI'95 Conference on Human Factors in Computing Systems, 1995. [31] B. Sheth and P. Maes. Newt: Evolving agents for personalized information ‾ltering. In Proceedings of the 9th Conference on Artificial Intelligence for Applications (CAIA-93), 1993. [32] Y. Y. Shih and D. R. Liu. Hybrid recommendation approaches: Collabora- tive ‾ltering via valuable content information. In Proceedings of the Proceed- ings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 8. IEEE Computer Society, 2005. [33] A. Stefani and C. Strapparava. Personalizing access to web sites: The siteif project. In Proceedings of 2nd Workshop on Adaptive Hypertext and Hypermedia HYPERTEXT'98, 1998. [34] K. Swearingen and R. Sinha. Beyond algorithms: An hci perspective on recom- mender systems. In ACM SIGIR 2001 Workshop on Recommender Systems, 2001. [35] J. Wang, A. P. Vries, and J. T. Reinders. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In SIGIR '06: Proceed- ings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pages 501-508, New York, NY, USA, 2006. ACM. [36] Satoshi Watanabe, Takayuki Ito, Tadachika Ozono, and Toramatsu Shintani. A paper recommendation mechanism for the research support system papits. In DEEC '05: Proceedings of the International Workshop on Data Engineer- ing Issues in E-Commerce, pages 71-80, Washington, DC, USA, 2005. IEEE Computer Society. [37] T. Yan and H. Garcia-Molina. Sift - a tool for wide-area information dissemi- nation. In USENIX Winter 1995 Technical Conference, 1995. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/41033 | - |
dc.description.abstract | 近年來,線上協同研究系統(Collaborative Research Systems)變得愈來愈熱門,因為它提供了一個良好的研究環境讓使用者可以在線上互相合作、管理並且分享彼此的文獻。傳統上使用者在分類自己的文獻時,常常因為不知道要如何明確分類而苦惱。而比較明確的分類法,像是ACM分類法(ACM Computation Classification),對於一般使用者更是難懂而難以使用。而大多數的協同研究系統之所以受歡迎的一項特色乃是它提供使用者利用標註(Tag)管理文獻的功能,因此使用者可以簡單自由地自行定義自己喜歡的關鍵字來管理組織自己的文獻。然而,使用標註來管理文獻雖然容易而且廣受歡迎,但是若使用者遇到自己不熟析的文獻時,使用者可能沒有辦法對此文獻下比較明確的標註。此外,當協同研究系統中的文獻累積到很巨大的數量時,使用者將會花更多的時間在尋找他們有興趣的文獻。
在此論文中,我們提出了一套標註推薦的機制來針對文獻推薦標註,用以協助使用者更能容易的管理文獻。此外,我們提出了兩套文獻推薦技術,分別是Tag Match和Tag-based CF,用以幫使用者找出他們有興趣閱讀的文獻。最後,我們利用從CiteULike擷取的資料來進行實驗,實驗結果說明我們所提出的標註與文獻推薦機制是簡單而且可行的。 | zh_TW |
dc.description.abstract | Collaborative research systems recently have grown in popularity on the web that allow users to collaborate on organizing and sharing bibliography online. Since it is difficult for users to categorize bibliography, especially in some professional way, e.g. the ACM computation classification, nowadays the most important feature provided by collaborative research systems is that they support tagging mechanisms so that users can easily and freely define their favorite keywords to organize bibliography. Although tagging is a simple and popular process for organizing bibliography, sometimes users still have difficulties to tag a bibliography if they are not familiar with it. Besides, if the bibliography collections in a collaborative research system have grown to a large amount, users will spend more and more time on finding bibliography in which they are interested.
In the thesis, we proposed a tag recommendation mechanism which can suggests tags to help users organize their bibliography, besides, we proposed two bibliography recommendation mechanisms, Tag Match and Tag-based CF, to recommend users bibliography they may be interested in. We conduct an experiment using the dataset clawed from CiteULike to show that the tag and bibliography recommendation mechanisms developed in this work are simple and feasible. | en |
dc.description.provenance | Made available in DSpace on 2021-06-14T17:12:58Z (GMT). No. of bitstreams: 1 ntu-97-R95922058-1.pdf: 966140 bytes, checksum: eb802ccb5482d57b1ffa87d274b3beab (MD5) Previous issue date: 2008 | en |
dc.description.tableofcontents | Acknowledgments ii
Abstract iii List of Figures ix List of Tables xi Chapter 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . .. 1 1.2 Problem De‾nition . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Thesis Organization . . . . . . . . . . . . . . . . . . . . . 4 Chapter 2 Related Work 5 2.1 Collaborative Research Systems . . . . . . . . . . . . . . . . . . . . . 5 2.1.1 CiteULike . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.2 Bibsonomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Recommender Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2.1 Demographic Approach . . . . . . . . . . . . . . . . . . . . . . 7 2.2.2 Content-Based Filtering Approach . . . . . . . . . . . . . . . 8 2.2.3 Collaborative Filtering Approach . . . . . . . . . . . . . . . . 9 2.2.4 Hybrid Approach . . . . . . . . . . . . . . . . . . . . . . . . . 10 Chapter 3 System Overview 11 3.1 System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.2 Bibliography Agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.3 Bibliography Management System . . . . . . . . . . . . . . . . . . . . 14 3.4 Bibliography Search Engine . . . . . . . . . . . . . . . . . . . . . . . 15 3.5 Tag Suggestion Engine . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.6 Bibliography Recommender System . . . . . . . . . . . . . . . . . . . 15 3.7 BibTeX format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.7.1 Entry types for BibTeX format . . . . . . . . . . . . . . . . . 16 3.7.2 Data Fields for BibTeX format . . . . . . . . . . . . . . . . . 18 Chapter 4 Data Analysis 23 4.1 CiteULike Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.2 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.2.1 User Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . 24 4.2.2 Tag Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . 25 4.2.3 Bibliography Data Analysis . . . . . . . . . . . . . . . . . . . 27 4.2.4 Data Sparsity Analysis . . . . . . . . . . . . . . . . . . . . . . 29 4.2.5 User Correlation Analysis . . . . . . . . . . . . . . . . . . . . 29 4.3 Conclusions for Data Analysis . . . . . . . . . . . . . . . . . . . . . . 31 Chapter 5 System Implementation 33 5.1 Implementation for Bib Agent . . . . . . . . . . . . . . . . . . . . . . 33 5.2 Implementation for Bibliography Management System . . . . . . . . . 34 5.3 Implementation for Bib Search Engine . . . . . . . . . . . . . . . . . 36 5.4 Implementation for Tag Suggestion Engine . . . . . . . . . . . . . . . 38 5.5 Implementation for Bibliography Recommender Systems . . . . . . . 40 5.5.1 The Tag Match Recommendation Mechanism . . . . . . . . . 41 5.5.2 The Tag-based Collaborative Filtering Recommendation Mech- anism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 Chapter 6 System Evaluation 45 6.1 Evaluation for Tag Suggestion Engine . . . . . . . . . . . . . . . . . . 45 6.1.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . 45 6.1.2 Evaluation Results . . . . . . . . . . . . . . . . . . . . . . . . 46 6.2 Evaluation for Biblography Recommender Systems . . . . . . . . . . 47 6.2.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . 47 6.2.2 Evaluation Results . . . . . . . . . . . . . . . . . . . . . . . . 48 Chapter 7 Conclusions 53 7.1 Summary of Contributions . . . . . . . . . . . . . . . . . . . . . . . . 54 Bibliography 55 | |
dc.language.iso | en | |
dc.title | 協同研究系統之標註與文獻推薦技術 | zh_TW |
dc.title | Tag and Bibliography Recommendations for Collaborative Research Systems | en |
dc.type | Thesis | |
dc.date.schoolyear | 96-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 薛智文(Chih-Wen Hsueh),黃乾綱(Chien-Kang Huang),鄭卜壬(Pu-Jen Cheng),陳穎平(Ying-ping Chen) | |
dc.subject.keyword | 論文推薦,標註推薦,推薦系統,協同過濾,標註,協同研究系統, | zh_TW |
dc.subject.keyword | bibliography recommendations,tag recommendations,recommendation system,collaborative filtering,tag,collaborative research systems, | en |
dc.relation.page | 59 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2008-07-28 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-97-1.pdf 目前未授權公開取用 | 943.5 kB | 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。