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Your search keyword '"Chen, Ching-Wen"' showing total 2 results
Start Over Author "Chen, Ching-Wen" Topic blogosphere Publisher elsevier b.v.
2 results on '"Chen, Ching-Wen"'

1. A synthetical approach for blog recommendation: Combining trust, social relation, and semantic analysis

Author

Li, Yung-Ming and Chen, Ching-Wen

Subjects
*ONLINE social networks, *BLOGS, *BACK propagation, *ARTIFICIAL neural networks, *INFORMATION retrieval, *RECOMMENDER systems
Abstract

Abstract: Weblog is a good paradigm of online social network which constitutes web-based regularly updated journals with reverse chronological sequences of dated entries, usually with blogrolls on the sidebars, allowing bloggers link to favorite site which they are frequently visited. In this study we propose a blog recommendation mechanism that combines trust model, social relation and semantic analysis and illustrates how it can be applied to a prestigious online blogging system – wretch in Taiwan. By the results of experimental study, we found a number of implications from the Weblog network and several important theories in domain of social networking were empirically justified. The experimental evaluation reveals that the proposed recommendation mechanism is quite feasible and promising. [Copyright &y& Elsevier]

Published
2009
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2. Discovering influencers for marketing in the blogosphere

Author

Li, Yung-Ming, Lai, Cheng-Yang, and Chen, Ching-Wen

Subjects
*BLOGS, *VIRAL marketing, *MATHEMATICAL models, *SOCIAL networks, *ARTIFICIAL neural networks, *INTERNET advertising, *SALES, *COMMERCIAL products
Abstract

Abstract: Discovering influential bloggers will not only allow us to understand better the social activities taking place in the blogosphere, but will also provide unique opportunities for sales and advertising. In this paper, we develop an MIV (marketing influential value) model to evaluate the influential strength and identify the influential bloggers in the blogosphere. We analyze three dimensions of blog characteristics (network-based, content-based, and activeness-based factors) and utilize an artificial neural network (ANN) to discover potential bloggers. Based on peer and official evaluations, the experimental results show that the proposed framework outperforms two social-network-based methods (out-degree and betweenness centrality algorithms) and two content-based mechanisms (review rating and popular author approaches). The proposed framework can be effectively applied to support marketers or advertisers in promoting their products or services. [Copyright &y& Elsevier]

Published
2011
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