Abstract
We propose a novel algorithm called Popularity&InfluenceCalculator (PIC) to get the most popular web pages and influent websites under certain keywords. We assume that the influence of a website is composed of its own significance and the effects of its pages, while the popularity of a web page is related with the websites and all the other pages. After that, we design a novel algorithm which iteratively computes importance of both websites and web pages. The empirical results show that the PIC algorithm can rank the pages in famous websites and pages with descriptive facts higher. We also find out that those pages contain more popular contents, which is accordant with our previous description of popularity. Our system can help users to find the most important news first, under certain keywords.
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Gao, S., Miao, Y., Yang, L., Li, C. (2009). Topic-Based Computing Model for Web Page Popularity and Website Influence. In: Nicholson, A., Li, X. (eds) AI 2009: Advances in Artificial Intelligence. AI 2009. Lecture Notes in Computer Science(), vol 5866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10439-8_22
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DOI: https://doi.org/10.1007/978-3-642-10439-8_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10438-1
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