基於社群行為之網頁推薦

淡江大學機構典藏 > 工學院 > 資訊工程學系暨研究所 > 學位論文 > Item 987654321/52358

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题名:	基於社群行為之網頁推薦
其它题名:	Recommended based on social behavior
作者:	鄭松棻;Cheng, Song-fen
贡献者:	淡江大學資訊工程學系碩士在職專班蔡憶佳
关键词:	資料探勘;網頁資料探勘;使用者分群;data mining;Web Data Mining;User Grouping
日期:	2010
上传时间:	2010-09-23 17:34:26 (UTC+8)
摘要:	隨著電腦科技的日益精進，網際網路的快速成長，促使了數位化資料大量的產生。網際網路變成一個龐大的資訊來源和提供豐富且有價值的資源，每一個Web站點就像是一個資料源，這些資料源可以看成廣泛意義上的資料庫，這比傳統意義上的資料庫更大、更複雜。透過網址連結，這些內容和組織都不同的Web站點就構成了一個巨大的異構資料庫環境。以目前全球資訊網上擁有的巨量資訊，如果沒有高效率搜尋引擎的幫助，尋找資訊將如同大海撈針一般困難，今天已有許多商業的搜尋引擎試圖滿足此類搜尋工作的需求，例如：Google，Yahoo，Ask與Microsoft Live Search:等。搜尋引擎多會依照某種方式進行排序，把相關的網路搜尋結果以排名順序列表一一提供使用者去瀏覽，讓使用者依照搜尋結果摘要的內容自行挑選。然而這樣的瀏覽方式極度沒有效率，因為網路搜尋結果通常相當的多，而一般使用者大多只會有耐心瀏覽搜尋結果的前若干筆，而且這類排名順序列表的呈現方式會使得很多關於使用者查詢的子議題通通混雜在一起，很容易造成使用者錯過重要資訊。此外，在檢索過程中，有許多使用者並非一直在進行關鍵字檢索，而是花費更多時間在瀏覽檢索的結果。然而，一個主要的問題是使用網頁內容與超連結方式的搜尋機制的搜尋引擎只能反映出網頁著作者的觀點而不是閱讀者的。在本論文中，我們根據使用者瀏覽網頁的內容發展網頁使用者群聚探勘技術。並且根據我們的實驗結果，可以透過使用者瀏覽過的網頁將網頁內容分群並且應用在網頁推薦上。 Pushed by the increasing advancement of computer technology and the rapid growth of the Internet, digital information has been produced on a mass scale. Internet network has become a huge information source and provided rich and valuable resource. Every Web site is like a data source, and these sources can be seen as a database in general sense, even large and more complex than the database in conventional sense. Via website links, these Web sites with different contents and organizations constitute a large heterogeneous database environment. Without the help of efficient search engines, finding the wanted information from the current World Wide Web will be as difficult as looking for a needle in the haystack. Today there are many commercial search engines to meet such needs, for instance: Google, Yahoo, Ask and Microsoft Live Search, and so on. Search engines usually will rate and list the searched results according to their relevancy for users to browse and choose the summary contents of the searched results. Such a browsing mode is extremely inefficient, since the quantity of web search results is usually quite huge and most general users only browse a number of searched results listed in the beginning. Besides, this kind of rating and listing would make a lot of sub-topics searched mixed up with the wanted ones. This also tends to cause users to miss important information. In addition, in the process of retrieval, many users usually do not keep conducting keyword searches but instead spending more time browsing the searched results. However, a major problem is that the search engines using the search mechanism of web contents and hyperlink mode can only reflect web authors’ views but not readers’. In this paper, we based on users’ browsing web contents to develop web user clustering mining technology. And according to our experimental results, users can classify the web contents (of the websites browsed by them) and apply those contents to the web recommendation through the websites browsed by them.
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