On the temporal dimension of search
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Trend detection through temporal link analysis
Journal of the American Society for Information Science and Technology - Special issue: Webometrics
Usage-Based PageRank for Web Personalization
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
BuzzRank … and the trend is your friend
Proceedings of the 15th 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
Link analysis using time series of web graphs
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
BrowseRank: letting web users vote for page importance
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Mining rich session context to improve web search
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A general markov framework for page importance computation
Proceedings of the 18th ACM conference on Information and knowledge management
A framework to compute page importance based on user behaviors
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
Page importance computation based on Markov processes
Information Retrieval
Recency-sensitive model of web page authority
Proceedings of the 21st ACM international conference on Information and knowledge management
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In the last years, a lot of attention was attracted by the problem of page authority computation based on user browsing behavior. However, the proposed methods have a number of limitations. In particular, they run on a single snapshot of a user browsing graph ignoring substantially dynamic nature of user browsing activity, which makes such methods recency unaware. This paper proposes a new method for computing page importance, referred to as Fresh BrowseRank. The score of a page by our algorithm equals to the weight in a stationary distribution of a flexible random walk, which is controlled by recency-sensitive weights of vertices and edges. Our method generalizes some previous approaches, provides better capability for capturing the dynamics of the Web and users behavior, and overcomes essential limitations of BrowseRank. The experimental results demonstrate that our method enables to achieve more relevant and fresh ranking results than the classic BrowseRank.