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1 DEIM Forum 2010 A3-3 Web Web Web Web Web. Web Abstract Web-page Recommendation System based on the Keyword transitions through a Web Exploration Junya EDA and Tetsuji SATOH School of Library and Information Science,University of Tsukuba 1 2,Kasuga,Tsukuba,Ibaraki, Japan Graduate School of Library Information and Media Studies,University of Tsukuba 1 2,Kasuga,Tsukuba,Ibaraki, Japan In recent years, The number of web-pages are increasing considerably. User cannot find a desired page without much burdens. We propose an efficient method to recommend web pages, which user want to visit. We create the directed graph which node are keyword between pages and estimate keyword transition from connection strength between the keyword. We recommend the Web-pages that a user will read with the keyword which we estimated. We evaluated that we used the keyword which I extracted by the system. we could estimate the pattern of the Web reading action and confirmed that the keyword which I estimated by a keyword transition graph functioned effectively. Key words Web exploration logs, co-occurrence frequency, recommendation 1. Web. Web URL

2 Web Web Web URL Web 2 3 Web Web 2. 1 Grineva [1] Wikipedia [2] Web [3] 2. 2 Web [4] web [5] [6] 2. 3 URL, Web URL 3. 2 Web Simpson Web 4. 3 (directed acyclic graph)

3 3 2 Simpson(Apple, ipod) Simpson X Y X Y X Y X Y Simpson 1 Simpson(X, Y ) = X Y min( X, Y ) (1) 2 Simpson mecab 1 Termextruct [7] Simpson Appleipod 2 Simpson [8] Simpson (2) Simpson(X, Y ) X Y if X > k and Y > k, = min( X, Y ) 0 otherwise 4 (2) 1http://mecab.sourceforge.net/

4 Web Simpson Simpson 3 2 iphone - iphone Simpson(,iphone) iphone 1 Apple Simpson(iphone,Aplle) iphone Applelog {Simpson(, iphone)} + log {Simpson(iphone, Apple)} Web 5 Web URL Web Web URL URL Web 4. 2 Web HTML Web HTML Contentextruct 2 HTML HTML divtd Web 4. 3 Contentextruct Web Termextruct [7] Web 7 CNET Japan 3 MOBILE CHANNEL 2http:// 3 CNET Japan

5 7 Web 10 4 Google Simpson Yahoo!WebsearchAPI 5 2 AND 2 Web,,, m n Simpson(m, n) Simpson 0.2 < = X < = 0.8 [8] Simpson Google, simpson (2) k Web Yahoo!WebserchAPI Web URL 4 CBS Interactive 5http://developer.yahoo.co.jp/ 8 Web Web Web 4 A. B. C. D. Web 5. 2 CNET Japan 1 3 CNET Japan D. 6http://japan.cnet.com/news/media/story /0, , ,00.htm

6 9 2 B., 3 C..Web < > Web Web..Simpson < >,Simpson () 4 1 D 2 B Bing Microsoft Bing Cashback LAN Mbps NTT Microsoft Bing Hurt comscore Bing Microsoft Google Wolfram Alpha 3 Android docomo PRO series NExus One docomo STYLE series NTT 3 C 4 A B C D A,B C,D

7 3 3 Forrester iphone iphone OS BlackBerry BlackBerry OS OS OS 2 iphone Google Google GS OS Andoroid 3 Nexus One Andoroid HTC Google U1 A B D U2 A B C U3 B A D U4 A B C U5 B C D U6 A B D 5 1 Bing 2 3 Google 8 5 Simpson Bing 2 3 Google NTT B () 9 B LANdocomoPROseries iphone OS Google 6 2 NTT 3 D iphone OS Google Web Web 8 9, 10 Simpson 2 Web Bing iphoneos Web Web URL

8 8 1 A Microsoft 5 Microsoft 8 Microsoft 5 Bing 4 Bing 4 Bing B docomoproseries 5 NTT 12 NTT 5 docomostyleseries 5 6 LAN D iphone Google 7 Android 5 OS 4 OS 5 4 iphone 4 3 Nexus One 3 OS 3 2 iphone OS 1 2 URL 6. Web Simpson Web Simpson Web 4 Web Web [1] Maria Grineva, Maxim Grinev, and Dmitry Lizorkin. Extracting key terms from noisy and multi-theme documents. WWW2009, Vol. Mining for Semantics, pp , [2],,,. Web., No , pp , [3],,,. Web Web. DEWS2008, B2-3, [4],,. web., Vol. 17, No. 2, pp , [5],.. JWEIN09, [6],,.., Vol. 49, No. 4, pp , [7],,.., Vol. 10, No. 1, pp , [8],,,,. Web., Vol. 20, No. 1, pp , 2005.

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