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dc.contributor.authorLiu, Duen-Renen_US
dc.contributor.authorOmar, Hanien_US
dc.contributor.authorLiou, Chuen-Heen_US
dc.contributor.authorChi, Huai-Chunen_US
dc.contributor.authorHsu, Cheng-Hoen_US
dc.description.abstractWeb 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.en_US
dc.subjectPopular event-based recommendationen_US
dc.subjectTrend analysisen_US
dc.subjectGoogle Insightsen_US
dc.subjectContent-based filteringen_US
dc.subjectItem-based collaborative filteringen_US
dc.titleRecommending blog articles based on popular event trend analysisen_US
dc.identifier.journalINFORMATION SCIENCESen_US
dc.contributor.department資訊管理與財務金融系 註:原資管所+財金所zh_TW
dc.contributor.departmentDepartment of Information Management and Financeen_US
Appears in Collections:Articles