Just test

2. Ref 寫法統一 標題大小寫 置左格式統一 流程圖圖形選用、圖形&箭頭說明 公式符號說明 投影片-老師的建議

3. [1] 修改動機: 修改目的(跟本來的技術比較，有何不同) 嚴謹的寫出Algo(照著實作，必須做得出來) [2] Click次數如何搜集? 預測有幾種方法? 推薦評分值的範圍?相乘後的結果分析? 10/1老師給的建議

4. 解決問題 理論上的 困難的應用問題 為什麼難? 貢獻在哪? 10/1老師給的建議(cont.)

5. A Survey on Service Personalization 學生：張維辰 指導教授：劉立頌 時間：2010/10/01

6. UMAP finished *1 IUI finished *1, finished but…*2 Related Conference(10/1)

7. User Modeling(UM, 1986-2007, 11th) California Adaptive Hypermedia and Adaptive Web-Based Systems(AH, 2000-2008, 5th) Italy UM+AH = UMAP(2009, 17th) UMAP(Adaption, Personalization)

8. Title : [1]Construction of Ontology-Based User Model for Web Personalization (Cited: 9 times) H. Zhang, Y. Song, and H.T. Song, “Construction of ontology-based user model for web personalization,” Proceeding of the 11th international conference User Modeling 2007, pp. 67–76. UM2007

9. Authors: Hui Zhang, Yu Song, and Han-tao Song Motivation: to provide web information that matches a user’s personal interests Purpose: Application: personalized web browsing and search [1]Construction of Ontology-Based User Model for Web Personalization

10. Semantic Web Usage Log Preparation Model(SWULPM) [1]How

11. Steps: 1.S-Log(Semantic-log):representing the semantics of the respective URL(from domain ontology) 2.Session analysis algorithm outcome : semantic session include thematic categories 3. IS = user’s new session=outcome B(IS):user ontology(beginning of the visit is empty) S(IS):structure of the site(automatically built) 4.O = B(IS) U O (O:global user’s ontology) [1]How(cont.)

12. :look up :union :ontology [1]How-Imagination of Ontology Global User Ontology

13. Each user has a graph: C_Graph(N, u)=<N, A>, N: nodes, A:arcs, u:user arc(s, t)=>label(s, t) = <dst, rst, hst, Tst> dst: semantic independence coefficient rst: semantic relevance coefficient hst: hit coefficient Tst: time coefficient s,t : concept [1]Pre-defined

14. Duration: 1997-2011 Title [1]Personalized News Recommendation Based on Click Behavior (Cited: 2 times) J. Liu, P. Dolan, and E.R. Pedersen, “Personalized news recommendation based on click behavior,” Proceeding of the 14th international conference on Intelligent User Interfaces, 2010, pp. 31–40. IUI 2010

15. Authors: Jiahui Liu, Peter Dolan, ElinRonby Pedersen(Google Inc.) Motivation: people was burdened with large online information Purpose: to help users find the information that are interesting to read Application: Google News [1]Personalized News Recommendation Based on Click Behavior

16. Click behavior advantage no ratings or negative votes after experiment (picture) news interests do change over time click distributions reflect the news trend different news trends in different locations news interests ↔ news trend in location (a certain extent) [1]How

17. Prediction User’s genuine interests The influence of local news trend Flow predicting user’s genuine news interest from a specific time period t combining predictions of past time periods predicting user’s current news interest recommendation [1]How(cont.)

18. [1]How(cont.) : predicting user’s current news interest : current news trend : past time user’s news interest Nt : all user’s clicks times in t time period G : the number of virtual clicks(smoothing factor)

19. Recommendation: (to rank a list of candidate articles) CR(article): content-based recommendation score CF(article): collaborative filtering recommendation score [1]How(cont.)

20. 遇到的問題 Semantic network與Ontology表達能力；經過學習，建構出符合個別使用者的user model，並依照feedback或觀察使用者的行為，進一步update user model，每個步驟是否真的透徹了解? 每個學習法(類神經網路、貝氏分類、最接近鄰居、決策樹)的差異為何?什麼情形選用哪種? 未來進度 UM 07，AH08，UMAP 09-10 IUI 09-10 目前進度

22. A Survey on Service Personalization 學生：張維辰 指導教授：劉立頌 時間：2010/09/10

23. Service Personalization Early research Overview of user-profile-based personalization User Profile Purpose Type Process of user-profile-based personalization Outline S.Gauch, M.Speretta, A. Chandramouli, and A. Micarelli, “User Profiles for Personalized Information Access, ” The Adaptive Web, LNCS 4321, pp.54-89

24. Early research Personalization

25. user-profile-based personalization Overview

26. Purpose To record interest or habit of the user To filter out irrelevant information from the user To identify additional information of likely interest for the user User Profile

27. Type Static ex: name, age, country, education level Dynamic short-term long-term User Profile

28. Process 1.Collecting information about users user identification user information collection explicit implicit 2.User Profile Representations 3.User Profile Construction User Profile

29. User identification Software agents Logins Enhanced proxy servers Cookies Session ids Collecting information about users

30. User identification(?)

31. Explicit Providing personal information (My Yahoo![110]) Rating (Web pages, Syskill&Webert[68];Movie, NetFlix[62];Consumer, ePinions[24]) Implicit Browsing history (OBIWAN [71]) Browsing activity ([71], Trajkova[99], Barrett[6]) All user activity (Seruku[83], Surfsaver[94]…) Search (Miserach[87], Liu[45]) User information collection

32. Keyword Profiles Amalthaea[61], Anatagonomy[78], Fab[5], Letizia[43], Syskill&Webert[68], PEA[60] Semantic Network Profiles Minio[56], SiteIF[92], InfoWeb[28], WIFS[53], AltaVista[3], ifWeb[4], Gasparetti[25,26] Concept Profiles Bloedorn[8], Sensus ontology[31,38], Yahoo!directory[42,111], OBIWAN[72] User Profile Representations

33. Extract from documents visited by the user during browsing Web pages Saved by the user Provide by the user Keyword Profiles

34. To solve the synonymproblem To solve the polysemy problem :planet :satellites Semantic network profiles

35. Semantic network profiles(cont.)

36. More abstract topics (not specific words or sets of related words) Concept profiles

37. User Profile Construction Building keyword profiles Building semantic network profiles Building concept profiles Thank you for attendance! Coming soon…

39. A Survey on Service Personalization 學生：張維辰 指導教授：劉立頌 時間：2010/09/17

40. GediminasAdomavicius , Alexander Tuzhilin, Using Data Mining Methods to Build Customer Profiles, Computer, v.34 n.2, p.74-82, February 2001 (Journal) Building Customer Profiles by data mining methods

41. Validation operator Similarity-based rule grouping Template-based rule filtering Redundant-rule elimination Profile-building process

43. Building keyword profiles Amalthaea[61] WebMate[13] Alipes[103] User Profile Construction

44. Building Keyword profiles

45. Amalthaea’s Ecosystem[61]

46. Key V=(W1, W2, W3, …, Wn) (待修改)Amalthaea’s Ecosystem[61](cont.) Web Pages Stemmer Html2txt filter Removal(commonly used) Html2url filter Hc x TF x IDF Moukas, A.: Amalthaea: Information Discovery And Filtering Using A Multi-agent Evolving Ecosystem. In: Applied Artificial Intelligence 11(5) (1997) 437-457 (Journal, Publisher : Taylor & Francis)

47. WebMate: A personal agent[13] Chen, L., Sycara, K.: A Personal Agent for Browsing and Searching. In: Proceedings of the 2nd International Conference on Autonomous Agents, Minneapolis/St. Paul, May 9-13, (1998) 132-139

48. Definition: 1. Profile set V = { V1, V2,…,VN} (N domains of interest for each user) 2. Document Di -> Vector Vi, i={1,…N} Vi={ e1,e2,…,eM}, ej =TF(wj, Di) x IDF(wj), j={1,…,M} WebMate[13](cont.)

49. Algorithm for multi TF-IDF vector learning: (待修改)WebMate[13](cont.) User marked “I like It” If |V| < N Add in set V T Parse HTML page F Compare every two vectors by (a) Extract TF-IDF vector Combine Vp, Vq with most similarity Vp = Vp + Vq Sort (a)

50. Widyantoro, D.H., Yin, J., El Nasr, M., Yang, L., Zacchi, A., Yen, J.: Alipes: A Swift Messenger In Cyberspace. In: Proc. 1999 AAAI Spring Symposium Workshop on Intelligent Agents in Cyberspace, Stanford, March 22-24 (1999)62-67 Alipes[103] Control

51. Alipes[103]

52. Coming soon… Thank you for listening Building semantic network profiles

53. A Survey on Service Personalization 學生：張維辰 指導教授：劉立頌 時間：2010/10/22

54. Authors Susan Gauch, Jason Chaffee and Alexander Pretschner Motivation It’s impossible to use one approach to browsing or searching for every user according to preference. Purpose Personalized web browsing and search Application Web sites Ontology-based personalized search and browsing (Cited: 194 times)

55. Reference ontology： Concept, Source Concept To extract top levels of the subject hierarchies (already existing) Source associated web pages from Yahoo, Magellan, Lycos, and the Open Directory Project How-Browsing

56. How

57. How

58. How-Mapping(1)

59. How-Mapping(2)

60. Now the site can have its content browsed using the personal ontology How-Mapping(3)

61. Approaches Re-ranking Filtering(X) How-Searching

62. (1)To extract only html tags(titles, summaries) (2)Classification (3)examine documents which belongs to User’s concepts (4)… How-Re-ranking

63. Thank you very much!!

64. A Survey on Text Categorization 學生：張維辰 指導教授：劉立頌 時間：2010/11/02

65. Classification supervised learning pre-defined categories ex. credit of consumer Clustering unsupervised learning unknown categories ex. similarity of consumer Preliminary

66. Motivation With the rapid growth of online information, it is difficult and time-consuming to deal with or classify the information by hand. Purpose To manage and use information easily Application Filter(personal portal site, email) Portal site Semantic identifier Image classification multimedia document classification Text Categorization(TC)

67. SVM(Support Vector Machine) Vapnik 1995 kNN(k-nearest neighbor) NB(Naïve Bayes) LLSF(Linear Least Squares Fit) NNet(Neural network) Approaches of TC

68. -------以下為中文--------

69. 建立在最小化結構風險理論上 將資料根據特徵轉成Rn空間中的向量，每筆資料可視為空間中的一點，並從Rn空間中找到一個n-1維的界線，稱為分類超平面-H。 H1, H2為支援超平面(Support hyperplane) SVM(1)

70. 多類別支援向量機 一對多 一對一 DAG(directed acyclic graph) method Considering all data at once method C&S method SVM(2)

71. SVM

72. kNN(1)

74. <message name="getTermRequest"> <part name="term" type="xs:string"/></message><message name="getTermResponse"> <part name="value" type="xs:string"/></message><portType name="glossaryTerms"> <operation name="getTerm"> <input message="getTermRequest"/> <output message="getTermResponse"/> </operation></portType> Example

75. One-way Request-response Solicit-response Notification Operation Types

76. Elements Envelope (root) Header mustUnderstandattr. actor attr. encoding style attr. Body Fault Namespace Soap Envelope Soap Encoding SOAP(Simple Object Access Protocol)

77. A Survey on Text Categorization Std. ：Wei-Chen Chang Prof.：Alan Liu 2010/11/18

78. 動機：資訊數位化，大量的電子文件分類，需耗費人力，且不客觀也缺乏一致性 目的：利用Ontology來協助分類，增加其準確性並節省人力、達到電子文件分類客觀與一致性 應用：線上即時新聞自動分類 做法：下頁 鐘明強, “基於Ontology架構之文件分類網路服務研究與建構”, 成功大學資訊工程所, 2004 基於Ontology架構之文件分類網路服務研究與建構

79. 系統架構 基於Ontology架構之文件分類網路服務研究與建構

80. Domain Weighted Ontology 以Object-Oriented Ontology為基礎，透過專家建構 Domain Ontology(政治、社會、氣象、運動、財經) 訓練此Domain Ontology成為Domain Weighted Ontology 概念 概念之間的關係 基於Ontology架構之文件分類網路服務研究與建構

81. 分類機制 基於Ontology架構之文件分類網路服務研究與建構

82. 五階層式模糊推論機制 1.輸入層(Input Layer) 2.輸入語意層(Input Linguistic Layer) 3.規規層(Rule Layer) 4.輸出語意層(Ouput Linguistic Layer) 5.輸出層(Output Layer) 模糊推論

83. 文字前處理網路服務 http://140.116.247.14/text_classification/text_classification.asmx?op=autotag 新聞分類網路服務 http://140.116.247.14/fuzzyclassification/service1.asmx?op=SRS 網路服務

84. 透過專家定義好的Ontology，來完成分類工作，不需要花很長的訓練時間。 學習到透過訓練資料，如何給予Ontology中的概念與關係權重值。 Ontology如何轉成Graph，並導出三個模糊變數 學到網路服務的基本觀念。 結論與吸收

85. 短期 如何建構適合的Ontology Ontology-based personalized search and browsing 的Reference 網路服務、網頁瀏覽、搜尋個人化相關的中文論文 模糊理論 洪正鑫, ”應用個人本體論於個人化網路服務選擇之研究” 中期 UMAP、IUI 未來目標