Object-level ranking: bringing order to Web objects
WWW '05 Proceedings of the 14th international conference on World Wide Web
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
BrowseRank: letting web users vote for page importance
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Freshness matters: in flowers, food, and web authority
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Ranking on large-scale graphs with rich metadata
Proceedings of the 20th international conference companion on World wide web
Ranking-based classification of heterogeneous information networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Page importance computation based on Markov processes
Information Retrieval
Large-scale graph mining and learning for information retrieval
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Introducing search behavior into browsing based models of page's importance
Proceedings of the 22nd international conference on World Wide Web companion
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We propose a General Markov Framework for computing page importance. Under the framework, a Markov Skeleton Process is used to model the random walk conducted by the web surfer on a given graph. Page importance is then defined as the product of page reachability and page utility, which can be computed from the transition probability and the mean staying time of the pages in the Markov Skeleton Process respectively. We show that this general framework can cover many existing algorithms as its special cases, and that the framework can help us define new algorithms to handle more complex problems. In particular, we demonstrate the use of the framework with the exploitation of a new process named Mirror Semi-Markov Process. The experimental results validate that the Mirror Semi-Markov Process model is more effective than previous models in several tasks.