Web サイト作成者によって設定された Web リンク システムが作成した Web リンク データ 静的リンク 学部ページ 大学 ページ 就職関連ページ 入試関連ページ 利用者の要求 データベース 動的リンク データ工学研究室ページ ベース研究室ページ 大学ページ 3. Web 学科ページ データ工
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1 DEWS2005 4B-o2 Web Web Web Web Web 1) Web 2) Web 1) Web 2) Web Web Web Web Web Web Web Web Abstract Automatic and Adaptable Web Links Generation Mechanism for Individual Users Keigo NAKATANI, Yu SUZUKI, and Kyoji KAWAGOE Graduate School of Science and Engineering, Ritsumeikan Univ. Nojihigashi 1-1-1, Kusatsu, Shiga, Japan Faculty of Science and Engineering, Ritsumeikan Univ. Nojihigashi 1-1-1, Kusatsu, Shiga, Japan In this paper, we propose a method for generating personalized Web links. These Web links are useful if users search Web pages related to the browsed Web pages. To generate these Web links, we proposed the following two processes, such as 1) the process of dividing the browsed Web pages into the meaningful blocks, and 2) the process of finding Web pages related to these blocks. We confirmed that, using our generated persornalized Web links, users can find the Web pages related to the users interests. Key words Hyperlink, Term Density Distributions, Assistance of Information Retrieval 1. Web Web Web Web Web Web
2 Web サイト作成者によって設定された Web リンク システムが作成した Web リンク データ 静的リンク 学部ページ 大学 ページ 就職関連ページ 入試関連ページ 利用者の要求 データベース 動的リンク データ工学研究室ページ ベース研究室ページ 大学ページ 3. Web 学科ページ データ工学研究室ページ データベース研究室ページ + ページ間の内容の類似度 学部ページ 学科ページ 就職関連ページ 入試関連ページ Web 1 Fig. 1 Web Change of the Web links when using a system. 1 Web Web 2. Web Web 2 2 Web K-means Web 3. 1 Step 6 Web Web 3. 1 Step1: Web <BODY> </BODY> Step2: HTML JavaScript [1]
3 ページ群 Web サイト Step1 タイトルアドレス本文のソース コンテンツ Step2 形態素解析 Step6 単語の出現密度分布 キーワード Step3 TF-ICF 法 Step5 K-means 法 重み付け Step4 類似度計算 LAB 前処理部 文章分割 Step B Web リンクの埋め込み位置 グループ化 Step A 関連ページの抽出 類似度 LAB1 top.html link.html カテゴリ : /LAB/ 利用者 キーワード k Web リンクの自動生成部 Fig. 2 最重要ページ P k まとまり 3 l P k l P k l P k Step A リンク先ページへの関連リンクの自動生成 2 The overview of our proposed system. k : キーワード P: ページ l : リンク index-j.html link.html research yamada research1.html research2.html image index.html hobby.html カテゴリ : /LAB/LAB1/ カテゴリ : /LAB/LAB1/yamada/ 3 Fig. 3 Creation of categories. 1 Step3: Step 2 TF-ICF(Term Frequency Inverse Category Frequency) [2] TF-ICF TF-IDF(Term Frequency Inverse Document Frequency) [3] TF-IDF (TF) (IDF) TF IDF TF-IDF Web TF-IDF TF-ICF TF-ICF TF-ICF (1) k j (j = 1, 2,, M i) p i (i = 1, 2,, N) w ij T F T ij ICF C j p i k j f ij Web O k j c j w ij = T ij C j = f ij (log O + 1) (1) c j [] 2 3 (1) (2) (1) index*.* top*.* main*.* home*.* O k u (u = 1, 2,, M i ) c u O c u k u (1) Step 1 URL (/) (3) (2) (2) (1) URL (3) (2) (3) (2) 3 (4) (3) (4) (1) Step4: [4] S p i P i (2) P i = [w i1, w i2,, w imi ] (2) p x p y e(p x, p y) (x, y = 1, 2,, N) (3)
4 Web ページ群 グループ化 [1 回目 ] ± (4) k j D j B j k j R j (v), (v = 1, 2,, B j ) k j A j グループ化 [2 回目 ] グループ化 [3 回目 ] D j = B j (R j(v) A j) 2 (4) B j v=1 Fig. 4 4 e(p x, p y ) = S(P x, P y ) Step5: = Grouping of the pages in a Website. M x+m y j=1 M x +M y j=1 w xj 2 (w xj w yj ) M x +M y j=1 w yj 2 Step 4 K-means [5] Web Step 4 Web K-means Step6: HTML HTML HTML 1 (1) p i(i = 1, 2,, N) (2) p i M i k j(j = 1, 2,, M i) (3) (3) (4) 1 HTML (a) (b) (c) (b) (c) (c) Web 3. 1 Web StepA: Step 5 Web StepB 1 HTML Table 1 HTML tag used in order to divide texts. <HR> <H > <P> <SPAN> <DIV> <BR><BR> <TABLE>
5 0 ユーザ 34 容量 1 インタフェース35 限界 2 技術 36 データ 3 www 37 ストリーム 4 方法 38 概念 5 データベース 39 データ 6 Web 40 データ 7 コンテンツ 41 流れ 8 効率 42 効率 9 コスト 43 データ 10 データベース 44 データ 11 Web 45 概念 12 サイト 46 ニュース 13 自動 47 天気 14 技術 48 情報 15 キーワード 49 ユーザ 16 構造 50 UI 17 Web 51 ユーザ 18 ページ 52 インタフェース 19 個人 53 目的 20 Web 54 手法 21 サイト 55 データ 22 My 56 いくつか 23 Web 57 フィルタ 24 サイト 58 クラス 25 データ 59 フィルタ 26 ストリーム 60 クラス 27 UI 61 値 28 情報 62 フロー 29 システム 63 ユーザ 30 センサー 64 情報 31 データ 65 方法 32 データ 33 データベース (a) ページの文章から出現順に抽出した単語 1 2 (b) 単語の出現密度分布と文章間の区切り 文章間の区切り 文章間を区切る単語 3 4 (c) ページの分割 まとまり1 Fig. 5 5 Example for division of a page. [ ] StepB: Web 3. 1 (3) リンク 3 K-means 法による Web サイト内のページ群に対するグループ化 選択したリンクを含むグループ Fig. 6 リンク 1 最重要ページ リンク 2 6 選択したリンクを含むグループ内のページ群に対するグループ化 選択したページ リンク 3 リンク 1 Web リンク 2 Automated generation of Web links. 選択したリンクを含むグループ内のページ群に対するグループ化 選択したページ (5) L E z(z = 1, 2,, D i) L E z E L リンク 2 E L = max e(l, E z) (5) リンク 1
6 γ 1 δ ϵ α = ϵ γ β = ϵ δ (6) (7) 埋め込まれたリンク 埋め込まれたリンク 7 Fig. 7 The page created by the system Step Step 6 α β % /30 86% 64% <table> Web Web : IT 2 Table 2 Accuracy of Web page division. / / / / /
7 最重要ページ 263 ページ番号 アクセス 1 Fig アクセス アクセス バリアフリー に対するリンク構造 アクセス Example of access using the proposal system. ページ 正解ページ Web 5. Web 5. 1 Web Web [6] [7] Web Web Table 3 3 Comparison of access frequencies for perusing correct answer pages * * 7 * * [8] Web Web [9] 5. 2 Vivisimo [10] GATA [11] [12] 5. 3 Deng Cai [13] VIPS (DocumentObjectModel) ( )
8 5. 4 [14] 6. Web Web Web HTML Web Web K-means ISODATA [15] X-means [16] [17] flash [1] :,, Vol. 41, No. 11, pp (2000). [2] Ko, Y. and Seo, J.: Automatic Text Categorization by Unsupervised Learning, Proceedings of the 17th conference on Computational linguistics (COLING-2000), pp (2000). [3] Salton, G., Lesk, M. E.( ): Introduction to Modern Information Retrieval, McGrawHill Book Co. (1983). [4] Salton, G., Wong, A. and Yang, C. S.: A vector space model for automatic indexing, Communications of the ACM, Vol. 18, No. 11, pp (1975). [5] Mac Queen, J.: Some Methods for Classification and Analysis of Multivariate Observations, Proceedings of the fifth Berkeley Symposium on Mathematical Statistics and Probability 1, pp (1967). [6], : Web, :, Vol. 42, No. 8, pp (2001). [7], : Web, :, Vol. 42, No. 8, pp (2001). [8],,,, : Web,, Vol. 132, No. 8, pp (2004). [9], : Web, 2 (FIT2003), pp (2003). [10] VivisimoInc.:. Vivisimo [11],,, :, 18 IPA (1999). [12] FujitsuBusinessSystemLTD.:. [13] Cai, D., Yu, S., Wen, J.-R. and Ma, W.-Y.: Extracting Content Structure for Web Pages based on Visual Representation, Proceedings of the 5th Asia Pacific Web Conference (2003). [14],,, : Web :, :, Vol. 99, No. 61, pp (1999). [15] ( ):, (1988). [16] Pelleg, D. and Moore, A.: X-means: Extending K-means with Efficient Estimation of the Number of Clusters, Proceedings of the Seventeenth International Conference on Machine Learning (ICML- 2000), pp (2000). [17] : k-means,, Vol. 29, No. 3, pp (2000).
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