Improved algorithms for topic distillation in a hyperlinked environment
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
SALSA: the stochastic approach for link-structure analysis
ACM Transactions on Information Systems (TOIS)
Modifications of Kleinberg's HITS algorithm using matrix exponentiation and web log records
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Improvement of HITS-based algorithms on web documents
Proceedings of the 11th international conference on World Wide Web
Ranking user's relevance to a topic through link analysis on web logs
Proceedings of the 4th international workshop on Web information and data management
Learning to Create Customized Authority Lists
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Truth discovery with multiple conflicting information providers on the web
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Comparing the effectiveness of hits and salsa
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Computing block importance for searching on web sites
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Truth Discovery with Multiple Conflicting Information Providers on the Web
IEEE Transactions on Knowledge and Data Engineering
Automatic online news topic ranking using media focus and user attention based on aging theory
Proceedings of the 17th ACM conference on Information and knowledge management
Evaluating importance of websites on news topics
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Heterogeneous network-based trust analysis: a survey
ACM SIGKDD Explorations Newsletter
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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.