Browsing by Author "Peng, Chi-Chieh"

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Combining Reputation and Content-Based Filtering for Blog Article Recommendation in Social Bookmarking Websites
(2010)
Peng, Chi-Chieh
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Liu, Duen-Ren
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資訊管理與財務金融系註：原資管所+財金所
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Department of Information Management and Finance
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. 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.
Hybrid content filtering and reputation-based popularity for recommending blog articles
(2014-01-01)
Liu, Duen-Ren
;
Liou, Chuen-He
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Peng, Chi-Chieh
;
Chi, Huai-Chun
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資訊管理與財務金融系註：原資管所+財金所
Purpose - Social bookmarking is a system which allows users to share, organise, search and manage bookmarks of web resources. However, with the rapid growth in the production of online documents, people are facing the problem of information overload. Social bookmarking web sites offer a solution to this by providing push counts, which are counts of users\' recommendations of articles, and thus indicate the popularity and interest thereof. In this way, users can use the push counts to find popular and interesting articles. A measure of popularity-based solely on push counts, however, cannot be considered a true reflection of popularity. The paper aims to discuss these issues. Design/methodology/approach - In this paper, the authors propose to derive the degree of popularity of an article by considering the reputation of the users who push the article. Moreover, the authors propose a novel personalised blog article recommendation approach which combines reputation-based group popularity with content-based filtering (CBF), for the recommendation of popular blog articles which satisfy users\' personal preferences. Findings - The experimental results show that the proposed approach outperforms conventional CBF, item-based and user-based collaborative filtering approaches. The proposed approach considering reputation-based group popularity scores on neighbouring articles indeed can improve the recommendation quality of traditional CBF method. Originality/value - The recommendation approach modifies CBF method by considering the target user\'s group preferences, to overcome the limitation of CBF which arises from the recommending only items similar to those the user has previously liked. Users with similar article preferences (profiles) may form a group of users with similar interests. A group\'s preferences may also reflect an individual\'s preferences. The reputation-based group preferences of the target user\'s group can be used to complement the target user\'s preferences.