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研究生:詹元順
論文名稱:於具有連結關係的網頁推薦中引入重要性之研究
論文名稱(外文):Adding Authoritiveness to Web Recommendation
指導教授:楊婉秀楊婉秀引用關係
學位類別:碩士
校院名稱:國立彰化師範大學
系所名稱:資訊管理學系所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:中文
論文頁數:52
中文關鍵詞:網頁推薦網頁連結架構PageRank資訊檢索使用者日誌記錄
外文關鍵詞:Web RecommendationWeb Link StructurePageRankInformation RetrievalWeb Usage Log
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隨著網際網路(World Wide Web)的蓬勃發展,在它上面分享的資訊量幾乎是每幾個月就成長一倍,早期資訊不足的問題在現今網際網路的時代已經不會再發生,取而代之的是資訊過量的問題。有鑒於此,本研究採用傳統分析內容(Content-based)方法分析網頁內容相似度,結合PageRank中Random Surfer之概念,建立網頁間連結架構以呈現網頁間連結關係,希望藉由引入網頁連結架構的分析,為使用者找出重要且相關的網頁,來判別出高品質(High-Quality)且具權威性(Authoritiveness)的網頁來進行推薦。利用本研究提出的兩種方法與傳統分析網頁內容進行推薦的方式,針對使用者瀏覽記錄評估推薦效益,實驗結果發現本研究所提出的兩種方法能得到較佳的推薦效果。
As the rapid growth of WWW websites, the amount of information in the Web has spawned on an unpredictable scale. We cannot effectively digest the information that we can get from the Web. In this paper, we therefore propose a novel approach that utilizes the concepts of classic content-based anlaysis, and combines PageRank for making web recommendation. Specifically, the proposed approach analyzes the link structure of the web pages. We try to identify that web pages has high-quality and authoritiveness for users. The experiment results show that our approach yields effective recommendation.
誌謝 i
摘要 ii
Abstract iii
目錄 iv
圖次 v
表次 vi
第一章 緒論 1
第二章 文獻探討 4
第一節 推薦系統 4
一、分析內容為基礎的網頁推薦方式 5
二、協同式為基礎的網頁推薦方式 10
三、網頁使用探勘 13
第二節 網頁在網際網路中的權威性 15
一、The InDegree Algorithm 17
二、The PageRank Algorithm 17
三、The HITS Algorithm 19
四、The SALSA Algorithm 20
第三節 網頁使用日誌 22
第三章 以連結關係為基礎的網頁推薦方法 24
第四章 實驗結果與討論 31
第一節 研究資料蒐集與處理 31
一、日誌(Logs)內容 31
二、網頁內容 36
第二節 實驗設計說明 37
一、精確度(Precision) 38
二、涵蓋率(Recall) 39
三、F1指標 39
第三節 實驗結果與討論 40
一、精確度(Precision) 40
二、涵蓋率(Recall) 42
三、F1指標 43
第五章 總結及未來研究 45
第一節 結論 45
第二節 研究限制 45
第三節 未來研究方向 46
參考文獻 47
[1] Agrawal, R. and Srikant, R. (1994). “Fast Algorithms for Mining Association Rules.” Proceedings of the VLDB Conference, pp. 487-499.
[2] Adomavicius, G. and Tuzhilin, T. (2003). “Recommendation Technologies: Survey of Current Methods and Possible Extensions.” Working Paper, Stern School of Business, New York University, New York, United States.
[3] Balabanovic, M. and Shoham, Y. (1997). “Fab: Content-based, Collaborative Recommendation.” Communications of the ACM, Volume 40, No.3, pp. 66-72.
[4] Brin, S. and Page, L. (1998). “The Anatomy of Large-Scale Hypertextual Web Search Engine.” Proceedings of the 7th International World Wide Web Conference, Brisbane, Australia.
[5] Cohen, Jacques (1992). “Special Issue on Information Filtering.” Communication of the ACM, Vol. 35, No. 12
[6] Cutting, D., Karger, D., Pedersen, J. and Tukey, J. (1992). “Scatter / gather: a cluster-based approach to browsing large document collections.” Proceeding of 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 318-329
[7] Chen, M.S., Park, J.S. and Yu, P.S. (1996). “Data Mining for Path Traversal Patterns in a web Environment.” Proceedings of the 16th ICDCS, pp. 385-392.
[8] Cooley, R., Mobasher, B. and Srivastava, J. (1997). “Web Mining: Information and Pattern Discovery on the World Wide Web.” Proceedings of the 9th IEEE International Conference on Tools with Artificial Intelligence (ICTAI’97), pp. 558-567.
[9] Cooley, R., Mobasher, B. and Srivastava, J. (1999). “Data preparation for mining World Wide Web browsing patterns.” Journal of Knowledge and Information Systems, 1(1), pp. 5-32.
[10] Dean, J. and Monika R. H. (1999). “Finding Related Pages in the World Wide Web.” Proceedings of the 8th International World Wide Web Conference, pp. 389–401.
[11] Estivill, C.V. and Lee, I. (2000). “AUTOCLUST: Automatic Clustering via Boundary Extraction for Massive Point Data Sets.” Proceeding of 5th International Conference Geo-Computation, University of Greenwich, Kent, UK. Aug. pp. 23-25
[12] Goffman, William (1971). “A mathematical method for analyzing the growth of a scientific discipline.” Journal of the ACM, 18(2):173{185.
[13] Goldberg, D. et al. (1992). “Using collaborative filtering to weave an information tapestry.” Communications of the ACM, Vol.35, No.12.
[14] Garfield, E. (1995). “New international professional society signals the maturing of sciento-metrics and informetrics.” The Scientist, 9(16).
[15] Herlocker, J., and Konstan, J. (2001). “Content-Independent task-focused recommendation.” IEEE Internet Computing, vol. 5 no 6, pp. 40-47.
[16] James E. Pitkow. (1997) “Characterizing World Wide Web Ecologies.” PhD thesis, Georgia Institue of Technology.
[17] Konstan, J. et al. (1997). “GroupLens: Collaborative Filtering for Usenet News.” Communication of the ACM, Mar., pp. 77-87.
[18] Kleinberg, J. M. (1998). “Authoritative Sources in a Hyperlinked Environment.” Proceedings of the 9th Annual ACM SIAM Symposium on Discrete Algorithms (SODA), pp. 668–677.
[19] Kao, Ben C.M., Lee Joseph K.W., Cheung David W.L., and Ng C.Y. (1998). “Recommending Anchor Points in Structure-Preserving Hypertext Document Retrieval.” Proceedings of the 22nd Annual International Computer Software and Applications Conference, pp. 582–587, IEEE.
[20] Kim, J.G. and Lee, E.S. (1999). “Intelligent Information Recommend System on the Internet.” In Proceedings of 1999 International Workshop on Parallel Processing, pp. 376-380.
[21] Kosala, R. and Blockeel, H. (2000). “Web Mining Research-A Survey.” The 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2(1), pp. 1-15.
[22] Lempel, R. and Moran, S., (2000). “The stochastic approach for link-structure analysis (SALSA) and the TKC effect.” Proceedings of the 9th International World Wide Web Conference.
[23] Marchiori, M., (1997). “The quest for correct information on Web: Hyper search engines.” Proceedings of the 6th International World Wide Web Conference.
[24] Michalski, R.S., Bratko, I., and Kubat, M. (1998). “Machine Learning and Data Mining Methods and Applications,” John Wiley and Sons Ltd, New York.
[25] Michael J. Pazzani (1999). “A Framework for Collaborative, Content-based and Demographic Filtering.” Artifical Intelligence Review, Volume 13, No.5-6, pp.393-408.
[26] Metern, R. v. and Someren, M. v. (2000). “Using Content-Based Filtering for Recommendation”, Proceedings of the ECML/MLNET 2000 Workshop on Machine Learning and the New Information Age, Barcelona, Spain, pp.47-56.
[27] Mobasher, B., Dai, H. Luo, T. Sun, Y. and Zhu, J. (2000). “Integrating Web Usage and Content Mining for More Effective Personalization.” Proceedings of the International Conference on E-Commerce and Web Technologies (ECWeb2000).
[28] Mobasher, B., Cooley, R., and Srivastava, J. (2000). “Automatic Personalization Based on Web Usage Mining.” Communications of the ACM (43:8), pp. 142-151.
[29] Mobasher, B., Dai, H., Luo, T., and Nakagawa, M. (2002). “Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization,” In Data Mining and Knowledge Discovery, Kluwer Publishing, Vol. 6, No. 1, pp. 61-82, January.
[30] Page, L., Brin, S., Motwani, R. and Winograd, T. (1998). “The PageRank Citation Ranking: Bringing Order to the Web.” Technical report, Stanford University, Stanford, CA.
[31] Pirolli, P., Pitkow, J. and Rao, R. (1996). “Silk from a sow's ear: Extracting usable structure from the web.” In Michael J. Tauber, Victoria Bellotti, Robin Je_ries, Jock D. Mackinlay, and Jakob Nielsen, editors, Proceedings of the Conference on Human Factors in Computing Systems : Common Ground, pages 118{125, New York, 13{18 April 1996. ACM Press.
[32] Pazzani, M. and Billsus, D. (1997). “Learning and Revising User Profiles: the Identification of Interesting Web Sites.” Machine Learning, Volume 27, No.3, pp.313-331.
[33] P. Resnick et al., (1994). “GroupLens: An Open Architecture for Collaborative Filtering of Netnews,” Proc. CSCW 94, ACM Press, New York, pp. 175-186.
[34] Resnick, P., and Varian, H. (1997). "Recommender Systems.” Introduction to special section of Communications of the ACM, Vol.40, No.37 , pp. 56-58
[35] Rucker, J. and Polanco, M.J. (1997). “Siteseer: Personalized Navigation for the Web,” Communications of the ACM (40:3), pp. 73-75.
[36] Sougata Mukherjea(1) and James D. Foley. (1995). “Showing the context of nodes in the world wide web.” Proceedings of ACM CHI'95 Conference on Human Factors in Computing Systems, volume 2 of Short Papers: Web Browsing, pp. 326
[37] Sougata Mukherjea(2), James D. Foley, and Scott Hudson. (1995). “Visualizing complex hypermedia networks through multiple hierarchical views.” Proceedings of ACM CHI'95 Conference on Human Factors in Computing Systems, volume 1 of Papers: Creating Visualizations, pp. 331
[38] Shardanand, Upendra and Pattie Maes, (1995). “Social Information Filtering: Algorithmsfor Automating ‘Word of Mouth.’” Proc. CHI 95, ACM Press, New York, pp. 210-217.
[39] Spertus, Ellen (1997). “Parasite: Mining structural information on the web.” Proceedings of the 6th International WWW Conference.
[40] Terveen, L., Hill, W., Amento, B., McDonald, D. and Creter, J. (1997). “PHOAKS: A System for Sharing Recommendations.” Communications of the ACM (40:3), pp. 59-62.
[41] Weiss, R., Velez, B., Sheldon, Mark A., Manprempre, C., Szilagyi, P., Duda, A. and David K. Gifford. (1996). “HyPursuit: A hierarchical network search engine that exploits content-link hypertext clustering.” Proceedings of the 7th ACM Conference on Hypertext, pages 180{193, New York, 16{20 March 1996. ACM Press.
[42] Yu, P.S. (1999). “Data Mining and Personalization Technologies.” In Proceedings of the 6th International Conference on Database System for Advanced Applications, pp. 6-13.
[43] 李維平,李政權,黃仁傑,黃寶嘉,(2000),「使用資料探勘技術產生個人化廣告之研究」,第二屆網站經營學術暨實務研討會論文集,101-110 頁,十二月。
[44] 馮文正,(2000),「合作式網站推薦系統」,國立交通大學資訊科學研究所碩士論文。
[45] 廖婉菁,(2002),「應用協同過濾機制於商品推薦之研究-以手機網站為例」,中原大學資訊管理學系。
[46] Hope, N. Tillman. “Evaluating quality on the net.”,http://www.hopetillman.com/findqual.html。
[47] CKIP中文詞知識庫小組,godel.iis.sinica.edu.tw/CKIP/index.htm
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