Browsing by Author "Chi, Huai-Chun"

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Hybrid content filtering and reputation-based popularity for recommending blog articles
(2014-01-01)
Liu, Duen-Ren
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Liou, Chuen-He
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Peng, Chi-Chieh
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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.
Recommending blog articles based on popular event trend analysis
(2015-06-01)
Liu, Duen-Ren
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Omar, Hani
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Liou, Chuen-He
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Chi, Huai-Chun
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Hsu, Cheng-Ho
Web 2.0 has become a popular social media on the Internet due to the fast evolution of Internet technologies, as well as increasing resources and users. Among the applications of Web 2.0, blogospheres are a new Internet social media for users to express their preferences and personal feelings. Most of the people tend to receive the newest information and articles related to popular issues. However, with the rapidly increasing number of active writers and viewers, it is hard for people to discover useful information that is beneficial or interesting to them. Accordingly, it is necessary to develop a recommendation approach that takes the emerging or popular events into consideration. In this work, we propose a novel event-based recommendation approach, which combines the event trend analysis and personal preference to recommend blog articles of popular events that suit user interests. We analyze blog articles to identify popular events, and then derive the popularity degrees of events based on blog-based popularity trend analysis and Google Insights-based popularity trend analysis. Our approach derives users\' personalized preferences on target articles of popular events by considering user interests (article-push records) and the predicted popularity degree of the events. Our recommendation methods improve recommendation accuracy by enhancing content-based filtering (CBF) and item-based collaborative filtering (ICF) with the event-based preference analysis. Our experiment result demonstrates that the proposed approach can effectively recommend users\' desired blog articles with respect to event popularity and personal interests. (C) 2015 Elsevier Inc. All rights reserved.