結合聲望與內容式過濾之書籤網站部落格文章推薦

Abstract

Web2.0 是一個新興的網路社群，提供一個平台讓網友互動、管理與分享資訊，像是部落格文章、書籤網站、網路影片、書評、產品意見等。書籤網站提供讓網友發表自己的文章或是推薦別人文章的功能，讓大家能更方便搜尋與分享熱門的文章或是自己感興趣的文章。但是隨著網路快速的發展，過多的網路訊息造成資訊過載的問題。以書籤網站為例，即使已經過濾處理，但每天還是有大量的文章推薦至書籤網站，而無法順利的消化如此龐大的資訊量。本研究提出了以文章熱門度為基礎，整合使用者聲望與內容式過濾之個人化部落格文章推薦方法，透過分析使用者過去的文章推薦情形，進一步推薦使用者感興趣的文章。實驗結果顯示本研究所提出的方法比傳統方法能更有效的針對使用者的興趣來推薦適合的部落格文章。

The new generation of web-based communities, Web2.0, represents an innovative spirit in sharing and managing contents. Social bookmarking is a portal for users to share, organize, search, and manage bookmarks of web resources. However, with the rapid growth of web documents that are produced every day, people are facing the problem of information overload. The Social bookmarking web site provides the push (user recommendation) counts of articles indicating the recommended popularity degrees of articles. Thus, users can refer the push counts to find popular and interesting articles. Popularity based solely on push counts, however, cannot truly reflect the trend of popularity. In this paper, we propose to derive the popularity degree of an article by considering the reputation of users that push the article. Moreover, we propose a personalized blog article recommendation approach, which combines the reputation-based popularity with content based filtering, to recommend popular blog articles to users that satisfy their personal preferences. Our experimental results show that the proposed approach outperforms conventional approaches.