Efficient crawling through URL ordering
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
A Theoretical Analysis of Google's PageRank
SPIRE 2002 Proceedings of the 9th International Symposium on String Processing and Information Retrieval
Scaling personalized web search
WWW '03 Proceedings of the 12th international conference on World Wide Web
Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search
IEEE Transactions on Knowledge and Data Engineering
ACM Transactions on Internet Technology (TOIT)
PageRank as a function of the damping factor
WWW '05 Proceedings of the 14th international conference on World Wide Web
VLDB '05 Proceedings of the 31st international conference on Very large data bases
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Link spam detection based on mass estimation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
SIAM Journal on Discrete Mathematics
BrowseRank: letting web users vote for page importance
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
PageRank for ranking authors in co-citation networks
Journal of the American Society for Information Science and Technology
Tracking the random surfer: empirically measured teleportation parameters in PageRank
Proceedings of the 19th international conference on World wide web
Distribution of PageRank mass among principle components of the web
WAW'07 Proceedings of the 5th international conference on Algorithms and models for the web-graph
Parallel browsing behavior on the web
Proceedings of the 21st ACM conference on Hypertext and hypermedia
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In large-scale commercial web search engines, estimating the importance of a web page is a crucial ingredient in ranking web search results. So far, to assess the importance of web pages, two different types of feedback have been taken into account, independent of each other: the feedback obtained from the hyperlink structure among the web pages (e.g., PageRank) or the web browsing patterns of users (e.g., BrowseRank). Unfortunately, both types of feedback have certain drawbacks. While the former lacks the user preferences and is vulnerable to malicious intent, the latter suffers from sparsity and hence low web coverage. In this work, we combine these two types of feedback under a hybrid page ranking model in order to alleviate the above-mentioned drawbacks. Our empirical results indicate that the proposed model leads to better estimation of page importance according to an evaluation metric that relies on user click feedback obtained from web search query logs. We conduct all of our experiments in a realistic setting, using a very large scale web page collection (around 6.5 billion web pages) and web browsing data (around two billion web page visits).