網際網路服務推薦系統

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姓名

吳緯閔(Wei-Min Wu) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

網際網路服務推薦系統
(A Web Services Recommendation System)

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摘要(中)

由於網頁資訊藉著分散式架構的網路環境快速擴散，造成網路上的資訊越來越龐大。在這樣的環境下，使用者取得最適當的網際網路服務不會是一件容易的事，因此為了協助人們快速找尋適合的網際網路服務，資訊過濾技術的引用便快速地發展起來，而推薦系統便是資訊過濾裡頭的一種智慧型網路技術。所以本論文便將推薦系統應用在網際網路服務的推廣和資訊獲得上。
一般來說推薦系統主要分成三種：協同式推薦系統和內容式推薦系統，還有混合兩種推薦系統的優點的混合式推薦系統。本論文設計一個混合式推薦系統，是以協同式推薦系統為主，以內容式推薦系統來彌補其不足。
由於協同式推薦系統需要對物品作分群，現行網際網路服務分群的技術，仍需要系統維護者手動分群，這樣的方式不但效率不佳，而且掺入人為因素。所以本論文提出了一個利用服務敘述之全自動網際網路服務分群機制，其中包括了自動分群機制，自動分群命名機制，自動化新增網際網路服務等。經實驗証實，本論文所提出之全自動網際網路服務分群的正確率可以達到80%，爾後利用本論文提出全自動網際網路服務分群機制所產生之分群，應用在推薦系統上。使用者滿意度亦有7.3分的高滿意度。

摘要(英)

In a large-scale distributed network environment like Internet, information has been increased and changed continuously. Accessing information in such dynamically changing, heterogeneous and world-wide distributed environments puts a big burden on the users. A possible solution to alleviate information overload is the use of recommendation systems. Recommendation Systems are a kind of web intelligence techniques to make daily information filtering for people.
In this paper, a web services recommendation system is proposed to help users to quickly retrieve the web services needed by them. To implement the web services recommendation system, we first develop a two-level clustering algorithm to automatically cluster web services into several groups. In addition, we propose a method to automatically search the most common characteristics from the services belonging to the same cluster to name the corresponding cluster. Based on the clustering results, appropriate Web services can then be effectively and quickly recommended to users. A possible solution to the “cold-start” problem is also implemented in the recommendation system. Simulation results demonstrated the performance of the proposed web services recommendation system is encouraging.

關鍵字(中)

★ 網際網路服務
★ 分群命名
★ 服務分群
★ 推薦系統

關鍵字(英)

★ Services Clustering
★ Web Services
★ Keywords: Recommendation System

論文目次

摘要 I
Abstract II
致謝 III
目錄 I
圖目錄 III
表目錄 V
一、緒論…………………………………………………………………………1
1-1 網際網路服務 1
1-2 推薦系統 2
1-3 研究動機與目的 3
1-4 論文架構 4
二、相關研究 5
2-1 網際網路服務分群 5
2-1-1 UDDI-註冊中心分類法 5
2-1-2 啟發式和非啟發式分類法 6
2-2 推薦系統種類 7
2-2-1 協同式推薦系統(Collaborative Filtering) 7
2-2-2 內容式推薦系統(Content-Based Filtering) 11
2-2-3 混合式推薦系統(Hybrid Approach) 12
2-3 網際網路服務推薦 13
2-4 WordNet 14
三、網際網路服務之推薦系統 16
3-1 系統架構 16
3-2 網際網路服務敘述 19
3-2-1 文字相似度公式 19
3-2-2 特徵擷取 20
3-3 網際網路服務分群 23
3-3-1 自我組織特徵映射演算法應用於服務分群 23
3-3-2 利用階層式演算法合併分群 25
3-3-3 網際網路服務分群命名 26
3-3-4 擴充新的網際網路服務 28
3-4 推薦系統 30
3-4-1 協同式推薦模組 30
3-4-2 以內容式推薦模組解決冷啟動問題 32
四、實驗結果與比較 34
4-1 資料蒐集 34
4-2 自我組織特徵映射演算法結果 35
4-3 階層式演算法合併結果 39
4-4 新增網際網路服務分類結果 43
4-5 網際網路服務推薦系統結果 45
五、結果與展望 47
5-1 結論 47
5-2 未來研究方向 48
參考文獻 49

參考文獻

[1] J. A. Alspector, A. Kolcz, and N. Karunanithi, “Comparing feature-based and clique-based user models for movie selection,” in Proceedings of the Third ACM Conference on Digital Libraries, Pittsburgh, Pennsylvania, USA, June 1998, pp. 11-18.
[2] M. Balabanovic and Y. Shoham, “Fab: content-based, collaborative recommendation,” Communications of the ACM, vol. 40, no. 3, pp. 66-72, March 1997.
[3] D. Billsus and M. J. Pazzani, “Learning collaborative information filters,” in Proceedings of the 15th International Conference on Machine Learning, San Francisco, USA, July 1998, pp. 46-54.
[4] J. S. Breese, D. Heckerman, and C. Kadie, “Empirical analysis of predictive algorithms for collaborative filtering,” in Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence, San Francisco, USA, 1998, pp. 43-52.
[5] A. Budanitsky and G. Hirst, “Semantic distance in wordNet: an experimental, application-oriented evaluation of five measures,” in Proceedings of Workshop on WordNet and Other Lexical Resources, second meeting of the North American Chapter of the Association for Computational Linguistics, Pittsburgh, USA, 2001, pp. 29-34.
[6] M. Bruno, G. Canfora, M. Di. Penta, and R. Scognamiglio, “An approach to support web services classification and annotation,” in Proceedings of the IEEE International conference on e-Technology, e-Commerce and e-Services, Hong Kong, March 2005, pp. 138-143.
[7] 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: Algorithms and Evaluation, Berkeley, California, USA, 1999, pp. 133-135.
[8] M. Á. Corella and P. Castells, “Semantic-based taxonomic categorization of web services,” in Lecture Notes in Computer Science, Springer Berlin, 2006, pp. 459-470.
[9] F. Curbera, M. Duftler, R. Khalaf, W. Nagy, N. Mukhi, and S. Weerawarana, “Unraveling the web services web: an introduction to SOAP, WSDL, and UDDI,” IEEE Internet Computing, vol. 6, pp. 86-93, April 2002.
[10] J. Delgado, N. Ishii, and T. Ura, “Content-based collaborative information filtering: actively learning to classify and recommend documents,” in Proceedings of the Second International Workshop on Cooperative Information Agents II, Learning, Mobility and Electronic Commerce for Information Discovery on the Internet, U.K., July 1998, pp. 206-215.
[11] B. Esfandiari, M. Weiss, and Y. Luo, “Towards a classification of web service feature interactions,” Computer Networks, vol. 51, pp. 359-381, February 2007.
[12] C. F. Eick, N. Zeidat, and Z. Zhao, “"Supervised clustering" algorithms and benefits,” in Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence, USA, November 2004, pp. 774-776.
[13] A. Hess and N. Kushmerick, “Automatically attaching semantic metadata to web services,” in Proceedings of the 2nd International Semantic Web Conference, New York, USA, October 2003.
[14] Q. Li and B. M. Kim, “Clustering approach for hybrid recommender system,” in Proceedings of the IEEE/WIC International Conference on Web Intelligence, South Korea, October 2003, pp. 33-38.
[15] S. M. Li, C. H. Chi, C. Ding, S. Chen, and Y. Huang, “Automatic recommendation of quality requirements for software services,” in Proceedings of the 2005 Australian Software Engineering Conference, Australian, August 2005, p. 117.
[16] G. Linden, B. Smith, and J. York, “Amazon.com recommendations item-to-item collaborative filtering,” IEEE Computer Society, vol. 7, pp. 76-80, February 2003.
[17] T. Joachims, “Text categorization with support vector machines: learning with many relevant features,” in 10th European Conference on Machine Learning, Germany, April 1998, pp. 137-142.
[18] N. Oldham, C. Thomas, A. S. Verma, and K. Verma, “METER-s web service annotation framework with machine learning classification,” in Proceedings of the 1st International Workshop on Semantic Web Services and Web Process Composition, California, USA, July 2004, pp. 137-146.
[19] P. Resnick, N. Iacovou, M. Suchak, P. Bergstorm, and J. Riedl, “GroupLens: An open architecture for collaborative filtering of netnews,” in Proceedings of the 1994 ACM conference on Computer supported cooperative work, North Carolina, USA, October 1995, pp. 210-217.
[20] R. J. F. Rossetti, S. Bampi, R. Liu, and D. V. Vliet, “An agent-based framework for the assessment of drivers’ decision-making,” in Proceedings of 2000 IEEE International Conference on Intelligent Transportation Systems, October 2000, pp. 387-392.
[21] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Analysis of recommendation algorithms for E-Commerce,” in Proceedings of the ACM E-Commerce 2000 Conference, Minnesota, USA, October 2000, pp. 158-167.
[22] U. Shardanand and P. Maes, “Social information filtering: algorithms for automating ‘Word of Mouth’,” in Proceedings of the Conference on Human Factors in Computing Systems, Denver, Colorado, USA, May 1995, pp. 210-217.
[23] G. Salton and M. J. McGill, Introduction to Modern Information Retrieval. McGraw-Hill, 1983.
[24] J. R. Saffran, E. Newport, and A. Aslin, “Word segmentation: the role of distributional cues,” Journal of Memory and Language, vol. 35, pp. 606-621, 1996.
[25] A. Selamat, H. Yanagimoto, and S. Omatu, “Web news classification using neural networks based on PCA,” in Proceedings of the 41st SICE Annual Conference 2002, August 2002, pp. 2389-2794.
[26] N. Thio and S. Karunasekera, “Automatic measurement of a QoS metric for web service recommendation,” in Proceedings of the 2005 Australian conference on Software Engineering, Australian, March 2007, pp. 202-211.
[27] H. Xia and T. Yoshida, “Dynamic selection of web services with recommendation system,” in Proceedings of the International Conference on Next Generation Web Services Practices, Seoul, August 2005.
[28] Amazon. [Online]. Available: http://www.amazon.com/ July 2008 [date accessed]
[29] Forbes. [Online]. Available: http://www.forbes.com/1999/10/04/feat.html July 2008 [date accessed]
[30] JWN. [Online]. Available: http://jwn.sourceforge.net/ July 2008 [date accessed]
[31] WordNet. [Online]. Available: http://wordnet.princeton.edu/ July 2008 [date accessed]
[32] X-methods. [Online]. Available: http://www.xmethod.net/ July 2008 [date accessed]
[33] 陳昭宇，「根基於自我組織特徵映射圖為基礎之最佳化演算法之推薦系統」，碩士論文，資訊工程研究所，國立中央大學，民國九十四年七月。
[34] 蕭仁傑，「以自適應共振理論建構網路服務管理系統之研究」，碩士論文，商業自動化與管理研究所，國立台北科技大學，民國九十五年七月。
[35] 蘇木春、張孝德，機器學習：類神經網路、模糊系統以及基因演算法則。全華科技圖書股份有限公司，台北市，民國八十年。
[36] 李昇暾、詹智安，JAVA Web Services 實務程式設計。旗標出版社，台北市，民國九十年。

指導教授

蘇木春(Mu-Chun Su)

審核日期

2008-7-20

推文