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維辰Survey101
Ref 寫法統一,[object Object],標題大小寫,[object Object],置左格式統一,[object Object],流程圖圖形選用、圖形&箭頭說明,[object Object],公式符號說明,[object Object],投影片-老師的建議,[object Object]
[1],[object Object],修改動機:,[object Object],修改目的(跟本來的技術比較,有何不同),[object Object],嚴謹的寫出Algo(照著實作,必須做得出來),[object Object],[2],[object Object],   Click次數如何搜集?,[object Object],預測有幾種方法?,[object Object],推薦評分值的範圍?相乘後的結果分析?,[object Object],10/1老師給的建議,[object Object]
解決問題,[object Object],理論上的,[object Object],困難的應用問題,[object Object],為什麼難?,[object Object],貢獻在哪?,[object Object],10/1老師給的建議(cont.),[object Object]
A Survey on Service Personalization,[object Object],學生:張維辰,[object Object],指導教授:劉立頌,[object Object],時間:2010/10/01,[object Object]
UMAP,[object Object],finished *1,[object Object],IUI,[object Object],finished *1, finished but…*2 ,[object Object],Related Conference(10/1),[object Object]
User Modeling(UM, 1986-2007, 11th),[object Object],California,[object Object],Adaptive Hypermedia and Adaptive Web-Based Systems(AH, 2000-2008, 5th),[object Object],Italy,[object Object],UM+AH = UMAP(2009, 17th),[object Object],UMAP(Adaption, Personalization),[object Object]
Title :,[object Object],[1]Construction of Ontology-Based User Model for Web Personalization (Cited: 9 times),[object Object],    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.,[object Object],UM2007,[object Object]
Authors: Hui Zhang, Yu Song, and Han-tao Song,[object Object],Motivation: to provide web information that matches a user’s personal interests,[object Object],Purpose:,[object Object],Application: personalized web browsing and search,[object Object],[1]Construction of Ontology-Based User Model for Web Personalization,[object Object]
Semantic Web Usage Log Preparation Model(SWULPM),[object Object],[1]How,[object Object]
Steps:,[object Object],1.S-Log(Semantic-log):representing the semantics of the respective URL(from domain ontology),[object Object],2.Session analysis algorithm,[object Object],	 outcome : semantic session include thematic categories,[object Object],3. IS = user’s new session=outcome,[object Object],   B(IS):user ontology(beginning of the visit is empty),[object Object],   S(IS):structure of the site(automatically built),[object Object],4.O = B(IS) U O (O:global user’s ontology),[object Object],[1]How(cont.),[object Object]
         :look up,[object Object],         :union ,[object Object],         :ontology,[object Object],[1]How-Imagination of Ontology,[object Object],Global User Ontology,[object Object]
Each user has a graph:,[object Object],C_Graph(N, u)=<N, A>, N: nodes, A:arcs, u:user,[object Object],arc(s, t)=>label(s, t) = <dst, rst, hst, Tst> ,[object Object],dst:  semantic independence coefficient,[object Object],rst:  semantic relevance coefficient,[object Object],hst:  hit coefficient,[object Object],Tst:  time coefficient,[object Object],s,t : concept,[object Object],[1]Pre-defined,[object Object]
Duration: 1997-2011,[object Object],Title ,[object Object],[1]Personalized News Recommendation Based on Click Behavior (Cited: 2 times),[object Object],    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.,[object Object],IUI 2010,[object Object]
Authors: Jiahui Liu, Peter Dolan, ElinRonby Pedersen(Google Inc.),[object Object],Motivation: people was burdened with large online information,[object Object],Purpose: to help users find the information that are interesting to read,[object Object],Application: Google News,[object Object],[1]Personalized News Recommendation Based on Click Behavior,[object Object]
Click behavior,[object Object],advantage,[object Object],no ratings or negative votes,[object Object],after experiment (picture),[object Object],news interests do change over time,[object Object],click distributions reflect the news trend,[object Object],different news trends in different locations,[object Object],news interests ↔ news trend in location (a certain extent) ,[object Object],[1]How,[object Object]
Prediction,[object Object],User’s genuine interests,[object Object],The influence of local news trend,[object Object],Flow,[object Object],predicting user’s genuine news interest from a specific time period t,[object Object],combining predictions of past time periods,[object Object],predicting user’s current news interest,[object Object],recommendation ,[object Object],[1]How(cont.),[object Object]
[1]How(cont.),[object Object],: predicting user’s current news interest,[object Object],      : current news trend,[object Object],      : past time user’s news interest,[object Object],Nt  : all user’s clicks times in t time period,[object Object],G   : the number of virtual clicks(smoothing factor),[object Object]
Recommendation:,[object Object],          (to rank a list of candidate articles),[object Object],CR(article): content-based recommendation score,[object Object],    CF(article): collaborative filtering recommendation score,[object Object],[1]How(cont.),[object Object]
遇到的問題,[object Object],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?,[object Object],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?,[object Object],未來進度,[object Object],UM 07,AH08,UMAP 09-10,[object Object],IUI 09-10,[object Object],目前進度,[object Object]
維辰Survey101
A Survey on Service Personalization,[object Object],學生:張維辰,[object Object],指導教授:劉立頌,[object Object],時間:2010/09/10,[object Object]
Service Personalization,[object Object],Early research,[object Object],Overview of user-profile-based personalization,[object Object],User Profile,[object Object],Purpose,[object Object],Type,[object Object],Process of user-profile-based personalization,[object Object],Outline,[object Object],S.Gauch, M.Speretta, A. Chandramouli, and A. Micarelli, “User Profiles for Personalized Information Access, ”  The Adaptive Web, LNCS 4321, pp.54-89,[object Object]
Early research,[object Object],Personalization,[object Object]
user-profile-based personalization ,[object Object],Overview,[object Object]
Purpose,[object Object],To record interest or habit of the user,[object Object],To filter out irrelevant information from the user,[object Object],To identify additional information of likely interest for the user,[object Object],User Profile,[object Object]
Type,[object Object],Static,[object Object],ex: name, age, country, education level,[object Object],Dynamic,[object Object],short-term ,[object Object],long-term,[object Object],User Profile,[object Object]
Process,[object Object],1.Collecting information about users,[object Object],user identification,[object Object],user information collection,[object Object],explicit,[object Object],implicit,[object Object],2.User Profile Representations,[object Object],3.User Profile Construction,[object Object],User Profile,[object Object]
User identification,[object Object],Software agents,[object Object],Logins,[object Object],Enhanced proxy servers,[object Object],Cookies,[object Object],Session ids ,[object Object],Collecting information about users,[object Object]
User identification(?),[object Object]
Explicit,[object Object],Providing personal information (My Yahoo![110]),[object Object],Rating (Web pages, Syskill&Webert[68];Movie,                  NetFlix[62];Consumer, ePinions[24]),[object Object],Implicit ,[object Object],Browsing history (OBIWAN [71]),[object Object],Browsing activity ([71], Trajkova[99], Barrett[6]),[object Object],All user activity (Seruku[83], Surfsaver[94]…),[object Object],Search (Miserach[87], Liu[45]),[object Object],User information collection ,[object Object]
Keyword Profiles,[object Object],Amalthaea[61], Anatagonomy[78], Fab[5], Letizia[43], Syskill&Webert[68], PEA[60],[object Object],Semantic Network Profiles,[object Object],Minio[56], SiteIF[92], InfoWeb[28], WIFS[53], AltaVista[3], ifWeb[4], Gasparetti[25,26],[object Object],Concept Profiles ,[object Object],Bloedorn[8], Sensus ontology[31,38], Yahoo!directory[42,111], OBIWAN[72],[object Object],User Profile Representations,[object Object]
Extract from documents visited by the user during browsing,[object Object],Web pages,[object Object],Saved by the user,[object Object],Provide by the user,[object Object],Keyword Profiles,[object Object]
To solve the synonymproblem,[object Object],To solve the polysemy problem,[object Object],                                                            :planet,[object Object],                                                          :satellites                                                                                             ,[object Object],Semantic network profiles,[object Object]
Semantic network profiles(cont.),[object Object]
More abstract topics (not specific words or sets of related words),[object Object],Concept profiles,[object Object]
User Profile Construction,[object Object],Building keyword profiles,[object Object],Building semantic network profiles,[object Object],Building concept profiles,[object Object],Thank you for attendance!,[object Object],Coming soon…,[object Object]
維辰Survey101
A Survey on Service Personalization,[object Object],學生:張維辰,[object Object],指導教授:劉立頌,[object Object],時間:2010/09/17,[object Object]
GediminasAdomavicius , Alexander Tuzhilin, Using Data Mining Methods  	 to  Build Customer Profiles, Computer, v.34 n.2, p.74-82, February 2001 (Journal),[object Object],Building Customer Profiles by data mining methods,[object Object]
Validation operator,[object Object],Similarity-based rule grouping,[object Object],Template-based rule filtering,[object Object],Redundant-rule elimination,[object Object],Profile-building process,[object Object]
維辰Survey101
Building keyword profiles,[object Object],Amalthaea[61],[object Object],WebMate[13],[object Object],Alipes[103],[object Object],User Profile Construction,[object Object]
Building Keyword profiles,[object Object]
Amalthaea’s Ecosystem[61] ,[object Object]
 Key V=(W1, W2, W3, …, Wn),[object Object],(待修改)Amalthaea’s Ecosystem[61](cont.)  ,[object Object],Web Pages,[object Object],Stemmer,[object Object],Html2txt filter,[object Object],Removal(commonly used),[object Object],Html2url filter,[object Object],Hc x TF x IDF,[object Object],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),[object Object]
WebMate: A personal agent[13],[object Object],    		 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,[object Object]
Definition:,[object Object],	1. Profile set V = { V1, V2,…,VN} ,[object Object],      (N domains of interest for each user),[object Object],	2. Document  Di  -> Vector Vi, i={1,…N},[object Object],	    Vi={ e1,e2,…,eM},  ,[object Object],ej =TF(wj, Di) x IDF(wj), j={1,…,M} ,[object Object],WebMate[13](cont.),[object Object]
Algorithm for multi TF-IDF vector learning:,[object Object],(待修改)WebMate[13](cont.),[object Object],User marked “I like It”,[object Object],If |V| < N,[object Object],Add in set V ,[object Object],T,[object Object],Parse HTML page,[object Object],F,[object Object],Compare every two vectors by (a),[object Object],Extract TF-IDF vector,[object Object],Combine Vp, Vq with most similarity,[object Object],Vp = Vp + Vq,[object Object],Sort,[object Object],(a),[object Object]
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,[object Object],Alipes[103],[object Object],Control,[object Object]
Alipes[103],[object Object]
Coming soon…,[object Object],Thank you for listening,[object Object],Building semantic network profiles,[object Object]
A Survey on Service Personalization,[object Object],學生:張維辰,[object Object],指導教授:劉立頌,[object Object],時間:2010/10/22,[object Object]
Authors,[object Object],Susan Gauch, Jason Chaffee and Alexander Pretschner,[object Object],Motivation,[object Object],It’s impossible to use one approach to browsing or searching for every user according to preference.,[object Object],Purpose,[object Object], Personalized web browsing and search,[object Object],Application,[object Object],Web sites,[object Object],Ontology-based personalized search and browsing (Cited: 194 times),[object Object]
Reference ontology: Concept, Source,[object Object],Concept,[object Object],To extract top levels of the subject hierarchies (already existing),[object Object],Source,[object Object],associated web pages from Yahoo, Magellan, Lycos, and the Open Directory Project,[object Object],How-Browsing,[object Object]
How,[object Object]
How,[object Object]
How-Mapping(1),[object Object]
How-Mapping(2),[object Object]
Now the site can have its content browsed using the personal ontology,[object Object],How-Mapping(3),[object Object]
Approaches,[object Object],Re-ranking,[object Object],Filtering(X),[object Object],How-Searching,[object Object]
(1)To extract only html tags(titles, summaries),[object Object],(2)Classification,[object Object],(3)examine documents which belongs to User’s concepts,[object Object],(4)…,[object Object],How-Re-ranking,[object Object]
Thank you very much!!,[object Object]
A Survey on Text Categorization,[object Object],學生:張維辰,[object Object],指導教授:劉立頌,[object Object],時間:2010/11/02,[object Object]
Classification,[object Object],supervised learning,[object Object],pre-defined categories,[object Object],ex. credit of consumer,[object Object],Clustering,[object Object],unsupervised learning,[object Object],unknown categories,[object Object],ex. similarity of consumer,[object Object],Preliminary,[object Object]
Motivation,[object Object],With the rapid growth of online information, it is difficult and time-consuming to deal with or classify the information by hand.,[object Object],Purpose,[object Object],To manage and use information easily,[object Object],Application,[object Object],Filter(personal portal site, email),[object Object],Portal site,[object Object],Semantic identifier,[object Object],Image classification,[object Object],multimedia document classification,[object Object],Text Categorization(TC),[object Object]
SVM(Support Vector Machine) Vapnik 1995,[object Object],kNN(k-nearest neighbor),[object Object],NB(Naïve Bayes),[object Object],LLSF(Linear Least Squares Fit),[object Object],NNet(Neural network),[object Object],Approaches of TC,[object Object]
-------以下為中文--------,[object Object]
建立在最小化結構風險理論上,[object Object],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。,[object Object],H1, H2為支援超平面(Support hyperplane),[object Object],SVM(1),[object Object]
多類別支援向量機,[object Object],一對多,[object Object],一對一,[object Object],DAG(directed acyclic graph) method,[object Object],Considering all data at once method,[object Object],C&S method,[object Object],SVM(2),[object Object]
SVM,[object Object]
kNN(1),[object Object]
[object Object],Definition,[object Object],<types>-data types,[object Object],<message>-parameters of a function call,[object Object],<portType>-function library,[object Object],<binding>-message format and protocol,[object Object],WSDL(Web Service Description Language),[object Object]
<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>,[object Object],Example,[object Object]
One-way,[object Object],Request-response,[object Object],Solicit-response,[object Object],Notification,[object Object],Operation Types,[object Object]
Elements,[object Object],Envelope (root),[object Object],Header,[object Object],mustUnderstandattr.,[object Object],actor attr.,[object Object],encoding style attr.,[object Object],Body,[object Object],Fault,[object Object],Namespace,[object Object],Soap Envelope,[object Object],Soap Encoding,[object Object],SOAP(Simple Object Access Protocol),[object Object]
A Survey on Text Categorization,[object Object],Std. :Wei-Chen Chang,[object Object],Prof.:Alan Liu,[object Object],2010/11/18,[object Object]
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,[object Object],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,[object Object],應用:線上即時新聞自動分類,[object Object],做法:下頁,[object Object],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,[object Object],基於Ontology架構之文件分類網路服務研究與建構,[object Object]
系統架構,[object Object],基於Ontology架構之文件分類網路服務研究與建構,[object Object]
Domain Weighted Ontology,[object Object],以Object-Oriented Ontology為基礎,透過專家建構,[object Object],   Domain Ontology(政治、社會、氣象、運動、財經),[object Object],訓練此Domain Ontology成為Domain Weighted Ontology,[object Object],概念,[object Object],概念之間的關係,[object Object],基於Ontology架構之文件分類網路服務研究與建構,[object Object]
分類機制,[object Object],基於Ontology架構之文件分類網路服務研究與建構,[object Object]
五階層式模糊推論機制,[object Object],1.輸入層(Input Layer),[object Object],2.輸入語意層(Input Linguistic Layer),[object Object],3.規規層(Rule Layer),[object Object],4.輸出語意層(Ouput Linguistic Layer),[object Object],5.輸出層(Output Layer),[object Object],模糊推論,[object Object]
文字前處理網路服務,[object Object],http://140.116.247.14/text_classification/text_classification.asmx?op=autotag,[object Object],新聞分類網路服務,[object Object],http://140.116.247.14/fuzzyclassification/service1.asmx?op=SRS,[object Object],網路服務,[object Object]
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。,[object Object],學習到透過訓練資料,如何給予Ontology中的概念與關係權重值。,[object Object],Ontology如何轉成Graph,並導出三個模糊變數,[object Object],學到網路服務的基本觀念。,[object Object],結論與吸收,[object Object]
短期,[object Object],如何建構適合的Ontology,[object Object],Ontology-based personalized search and browsing 的Reference,[object Object],網路服務、網頁瀏覽、搜尋個人化相關的中文論文,[object Object],模糊理論,[object Object],洪正鑫, ”應用個人本體論於個人化網路服務選擇之研究”,[object Object],中期,[object Object],UMAP、IUI,[object Object],未來目標,[object Object]

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