Browsing by Author "Liou, Chuen-He"

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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.
Hybrid Multiple Channels-based Recommendations for Mobile Commerce
(2010)
Liou, Chuen-He
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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
Mobile data communications have evolved as the number of third generation (3G) subscribers has increased to conduct mobile commerce. Multichannel companies would like to develop mobile commerce but meet difficulties because of lack of knowledge about users' consumption behaviors on the new mobile channel. Typical collaborative filtering (CF) recommendations may suffer from the so-called sparsity problem because few products are browsed on the mobile Web. In this study, we propose a hybrid multiple channels method to resolve the lack of knowledge about users' consumption behaviors on the new channel and the difficulty of finding similar users due to the sparsity problem of the typical CF. Products are recommended to the new mobile channel users based on their browsing behaviors on the new mobile channel as well as consumption behaviors on the existing multiple channels according to different weights Our experimental results show that the proposed method performs well compared to the other recommendation methods
Hybrid recommendations for mobile commerce based on mobile phone features
(2012-05-01)
Liou, Chuen-He
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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
Mobile data communications have evolved as the number of third generation (3G) subscribers has increased. The evolution has triggered an increase in the use of mobile devices, such as mobile phones, to conduct mobile commerce and mobile shopping on the mobile web. There are fewer products to browse on the mobile web; hence, one-to-one marketing with product recommendations is important. Typical collaborative filtering (CF) recommendation systems make recommendations to potential customers based on the purchase behaviour of customers with similar preferences. However, this method may suffer from the so-called sparsity problem, which means there may not be sufficient similar users because the user-item rating matrix is sparse. In mobile shopping environments, the features of users' mobile phones provide different functionalities for using mobile services; thus, the features may be used to identify users with similar purchase behaviour. In this paper, we propose a mobile phone feature (MPF)-based hybrid method to resolve the sparsity issue of the typical CF method in mobile environments. We use the features of mobile phones to identify users' characteristics and then cluster users into groups with similar interests. The hybrid method combines the MPF-based method and a preference-based method that uses association rule mining to extract recommendation rules from user groups and make recommendations. Our experiment results show that the proposed hybrid method performs better than other recommendation methods.
Mobile commerce product recommendations based on hybrid multiple channels
(2011-01-01)
Liu, Duen-Ren
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Liou, Chuen-He
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資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
The number of third generation (3G) subscribers conducting mobile commerce has increased as mobile data communications have evolved. Multi-channel companies that wish to develop mobile commerce face difficulties due to the lack of knowledge about users' consumption behavior on new mobile channels. Typical collaborative filtering (CF) recommendations may be affected by the so-called sparsity problem because relatively few products are browsed or purchased on the mobile Web. In this study, we propose a hybrid multiple channel method to address the lack of knowledge about users' consumption behavior on a new channel and the difficulty of finding similar users due to the sparsity problem of typical CF recommender systems. Products are recommended to users based on their browsing behavior on the new mobile channel as well as the consumption behavior of heavy users of existing channels, such as television, catalogs, and the Web. Our experiment results show that the proposed method performs well compared to the other recommendation methods. (C) 2010 Elsevier B.V. All rights reserved.
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.