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1 22 Retrieval support system using bookmarks that are shared in an organization
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3 Abstract Retrieval support system using bookmarks that are shared in an organization Yoshihiko Komaki In an organization, it is very important that we make use of the information using personal knowledge in the organization in order to obtain knowledge. Since early times, various forms of information sharing is done. In recently, the method which efficiently shares information is proposed by information sharing system using IT tecnology. In existing research, there is a system recommended shared bookmark to the hobbies and diversions of the user. As a problems, shared bookmark is not classified. So right recommendation is not performed. In this thesis, developed a system classifies shared bookmark automatically, and developed and suggested a system more recommended shared bookmark to the hobbies and diversions of the user. We compared the suggestion system with the existing system. As a result, suggested system was better than an existing system. key words Information sharing, retrieval support, Social Bookmark ii
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8 1 1.1 Web [1] Web Google Web [2] Web RSS Web Web RSS Web RSS [3] 1 [4] 1
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10 2 2.1 IT IT IT 3 3
11 Web 4
12 Web Livedoor 2.2 5
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14 3 [5] 3.1 Web Web
15 ID URL 3.1 ID URL A PHP Java... A Java eclipse SQL... B C Apple Mac ipod itunes
16 ID ID 3.2 ID A PHP JavaScript SQL MySQL Apache Java... B... C Java eclipse
17 (3.1) = (3.1) %
18 3.3 PHP PHP 11
19 Livedoor [6] 12
20
21 4.3 [7] Probabilistic Latent Semantic Indexing PLSI [8] PLSI 1 d w z 4 p(d, w) = z p(z)p(w z)p(d z) (4.1) D = (d 1,d 2,...,d N ) D W = (w 1,w 2,...,w M ) (4.2) L = N M n(d i, w j )logp(d i, w j ) (4.2) i=1 j=1 EM E-step Q (t+1) ijk = p(z k ) (t) p(w j z k ) (t) p(d j z k ) (t) K k=1 p(z k) (t) p(w j z k ) (t) p(d j z k ) (t) (4.3) M-step p(w j z k ) (t) = N i=1 n(d i, w j )Q (t) ijk N i=1 M j=1 n(d i, w j )Q (t) ijk (4.4) 14
22 4.3 p(z k ) (t) = K k=1 N i=1 M j=1 n(d i, w j )Q (t) ijk N i=1 M j=1 n(d i, w j )Q (t) ijk (4.5) p(d i z k ) (t) = M j=1 n(d i, w j )Q (t) ijk N i=1 M j=1 n(d i, w j )Q (t) ijk (4.6) E-step M-step p(w j z k ) d i z k p(d i z k ) p(d i z k ) p(d i z k ) 文書 クラスタ クラスタ 1 クラスタ 2 クラスタ 3 クラスタ 4 文書 文書 文書 文書 文書 文書 PHP PHP 15
23 4.3 PHP PHP (A,B,C,D,...) PHP 1 A G L O... 2 D H J N... 3 B E F M... 4 C I K P
24 PHP PHP PHP PHP 4 4 PHP PHP
25
26 PHP 30 PHP 38 PHP (5.1) = 100 (5.1) PLSI %
27 PHPに関するページ 1 回目 2 回目 4 回目 5 回目 6 回目 7 回目 8 回目 9 回目 10 回目 動作サンプル プログラム 概要 説明 PHPに関するページ 11 回目 12 回目 14 回目 15 回目 16 回目 17 回目 18 回目 19 回目 20 回目 動作サンプル プログラム 概要 説明 (5.2) = (5.2) % % 47.6% 71.3% PLSI 20
28
29 6 PLSI PLSI 47.6% 72.6%
30 23
31 [1] - Enterprise ZDNet Japan, [2] Jesse Alpert and Nissan Hajaj. We knew the web was big.... Official Google Blog [3] Karger,D.R. and Quan,D., What would it mean to blog on the semantic web?, in Proc. Third International Semantic Web Conference, pp ,2004. [4],, 19, 2008 [5], Web,,, [6] Livedoor , [7] Ver.2.3.3,, [8] Thomas Hofmann, Probabilistic Latent Semantic Indexing, the 22nd annual international ACM SIGIR conference on Research and development in infomation retrieval, ACM, pp.50-57,
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