Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/19530
標題: 植基於個人本體論模型與合作式過濾技術之中文圖書館推薦系統
A Library Recommender System Based on Personal Ontology Model and Collaborative Filtering Technique for Chinese Collections
作者: 許正怡
Hsu, Cheng-Yi
關鍵字: Ontology;本體論;Collaborative Filtering;Recommender System;Information Filtering;合作式過濾;推薦系統;資訊過濾
出版社: 資訊科學與工程學系所
引用: [1]卜小蝶。「淺析個人服務技術的發展趨勢對圖書館的影響」。國立成功大學圖書館館刊 2期(1998,10),pp.63-73。1998。 [2]王文君。「初探Ontology」。台灣大學建築與城鄉研究所博士生。 [3]吳美美。「中文資訊系統使用研究」 (pp.119-142)。台北市:台灣學生。2001。 [4]何光國。「圖書館學理論基礎」 (pp.349-364)。台北市:三民書局。1999。 [5]阮明淑、溫達茂。「Ontology應用於知識組織之初探」。佛教圖書館館訊第32期(91年12月),pp.6-17。2002。 [6]陳友民。「中國圖書分類法」。國立中央大學圖書館通訊,第三十五期, 91年 12月,2002。 [7]陳慧玲。「基植於個人本體論的圖書推薦系統」。中興大學資訊科學研究所碩士論文,台中市。2007。 [8]廖宜恩、許雯絞、鄭名珊。「植基於個人本體論與合作式過濾的英文館藏推薦系統」。二○○八數位生活科技研討會,成功大學,台中,台灣,2008.6.6。 [9]鄭惠珍。「中國圖書分類法」之探討-以實例論證。大學圖書館3卷3期(民88年7月),頁129-148。(1999)。 [10]韓瑛馡、高國峰、廖宜恩。「圖書館網站個人化推薦系統研究」。台灣網際網路研討會,TANNET2005。2005。 [11] N. J. Belkin and W. B. Croft, “Information filtering and information retrieval: two sides of the same coin?,” Communications of the ACM, vol. 35, Issue 12, Special issue on information filtering, December 1992, pp.29-38. [12] R. M. Bell and Y. Koren, ”Improved Neighborhood-based Collaborative Filtering,” KDDCup’07, August 12, 2007, San Jose, California, USA. [13] J. S. Breese, D. Heckerman and C. Kadie, “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” Uncertainty in Artificial Intelligence, Proceedings of the Fourteenth Conference, Morgan Kaufman, 1998, pp. 43-52. [14] D. Brickley and R.V. Guha, “RDF Vocabulary Description Language 1.0: RDF Schema,” http://www.w3.org/TR/2002/WD-rdf-schema-20021112/, W3C Working Draft, November 12, 2002. [15] B. Chandrasekaran, J. R. Josephson and V. R. Benjamins, “What Are Ontologies, and Why Do We Need Them?,” IEEE Intelligent Systems, vol. 14, Issue 1, Jaunary 1999, pp. 20-26. [16] M. Deshpande and G. Karypis, ”Item-Based Top-N Recommendation Algorithms,” ACM Transactions on Information Systems (TOIS), vol. 22, Issue 1, January 2004, pp. 144-176. [17] D. Goldberg, D. Nichols, B.M. Oki and D. Terry, “Using collaborative filtering to weave an information tapestry,” Communications of the ACM, vol. 35, Issue 12, December 1992, pp. 61-70. [18] J. Hendler, “Agents and the Semantic Web,” IEEE Intelligent Systems Journal, vol. 16, Issue 2, March 2001, pp. 30-37. [19] J. L. Herlocker, J. A. Konstan and J. T. Riedl, “Explaining collaborative filtering recommendations,” Proceedings of the 2000 ACM conference on Computer supported cooperative work, Philadelphia, Pennsylvania, United States, 2000, pp. 241-250. [20] J. L. Herlocker, J. A. Konstan, L. G. Terveen and J. T. Ridel, “Evaluating collaborative filtering recommender systems,” ACM Transactions on Information Systems (TOIS), vol. 22, Issue 1, pp. 5-53, January 2004. [21] I-En Liao, Shu-Chuan Liao, Kuo-Fong Kao and Ine-Fei Harn, “A Personal Ontology Model for Library Recommendation System,” Proceedings of 9th International Conference on Asian Digital Library, S. Sugimoto et al. (Eds.), Lecture Notes in Computer Science, vol. 4312, Springer-Verlag, November 2006, pp. 173-182. [22] D. L. McGuinness and F. Harmelen, “OWL Web Ontology Language Overview,” http://www.w3.org/TR/2004/REC-owl-features-20040210/#ref-rdf-schema,W3C Recommendation, February 10, 2004. [23] E. L. Morgan, “MyLibrary: A Model for Implementing a User-centered, Customizable Interface to a Library’s Collection of Information Resources,” e-Print :arXiv:cs/9902003v1 [cs.DL]. [24] N. F. Noy and D. L. McGuinne, “Ontology Development 101: A Guide to Creating Your First Ontology,” Stanford Medical Informatics Technical Report SMI-2001-0880, March 2001. [25] P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom and J. Riedl, ”GroupLens: an open architecture for collaborative filtering of Netnews,“ Proceedings of the 1994 ACM conference on Computer supported cooperative work, Chapel Hill, North Carolina, United States, October 22 - 26, 1994 , pp. 175-186. [26] B. Sarwar, G. Karypis, J. A. Konstan and J. T. Riedl, “Analysis of Recommendation Algorithms for Ecommerce,” Proceedings of the 2nd ACM Conference on Electronic Commerce, October 17-20, 2000, pp. 158-167. [27] B. Sarwar, G. Karypis, J. A. Konstan and J. T. Riedl, “Item-based collaborative filtering recommendation algorithms,” Proceedings of the 10th International World Wide Web Conference (WWW10), Hong Kong, May 1-5, 2001, pp. 285-295. [28] A. I. Schein, A. Popescul and D. M. Pennock, “Generate Models for Cold-Start Recommendations,” Proceedings of the 25''th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2002), Tampere, Finland, August 11-15, 2002, pp.253-260. [29] U. Shardanand and P. Maes, ”Social Information Filtering: Algorithms for Automating Word of Mouth,” Conference on Human Factors in Computing Systems, Denver, Colorado, United States, May 7-11, 1995, pp. 210-217. [30] O. Udrea, L. Getoor and J. Miller, “Leveraging Data and Structure in Ontology Integration,” Proceedings of the 2007 ACM SIGMOD international conference on Management of data, Beijing, China, June 11-14, 2007, pp. 449-460. [31] J. Wang, A. P. Vries and M. J. T. Reinders, “Unifying User-based and Item-based Collaborative Filtering Approaches by Similarity Fusion,” Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, Seattle, Washington, USA, August 6-11, 2006, pp. 501-508. 網路資源 [32]中文斷詞系統,線上檢索日期:2008/5/15,http://ckipsvr.iis.sinica.edu.tw/。
摘要: 
本實驗室開發的個人本體論圖書館推薦系統(PORE: Personal Ontology Recommender System)是以中國圖書分類法為參考本體論(reference ontology),在經過有效率的整理讀者個人過去的借閱紀錄後,建立個人本體論(personal ontology)做為推薦之依據。當讀者登錄至推薦系統時,在不使用檢索功能之前提下,系統即可根據個人本體論,自動推薦讀者個人所感興趣的相關知識主題下的館藏,以協助讀者在龐大的圖書館館藏之中能快速取得他想要的圖書資源。
本研究延續個人本體論模型,結合合作式過濾相關技術,依據圖書館讀者間過去借閱行為最相似的一群讀者之個人本體論,找出最受關注且未被個別讀者探索的知識主題推薦清單,再依據知識主題內容的重要度高低推薦給讀者個人。本系統已完成實作,將可在圖書館個人化主題推薦的領域提供更廣泛、實用的服務。

The research team in our lab has developed a library recommender system called Personal Ontology REcommender System (PORE), which is in service at the Library of National Chung-Hsing University. The PORE system uses Classification Scheme for Chinese Libraries (CCL) as Reference Ontology and builds the Personal Ontology for each patron by mining patron's loan records.
In this paper, we incorporate collaborative filtering technique with Personal Ontology Model of PORE. Collaborative filtering approach assumed that those who agreed in the past tend to agree again in the future. Therefore, the system aggregates those who have most similar Personal Ontology with a particular patron and then provides a recommended book list from common interested topics. The enhanced version of the PORE system is also demonstrated in this paper to show the effectiveness of the proposed approach.
URI: http://hdl.handle.net/11455/19530
其他識別: U0005-1308200809241600
Appears in Collections:資訊科學與工程學系所

Show full item record
 

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.