We address the task of learning rankings of documents from search engine logs of user behavior. Previous work on this problem has relied on passively collected clickthrough data. ...
Evaluating user preferences of web search results is crucial for search engine development, deployment, and maintenance. We present a real-world study of modeling the behavior of ...
Eugene Agichtein, Eric Brill, Susan T. Dumais, Rob...
The collective contributions of billions of users across the globe each day result in an ever-changing web. In verticals like news and real-time search, recency is an obvious sign...
A re-ranking technique,called “PageRank brings a successful story behind the search engine. Many studies focus on finding an way to compute the PageRank scores of a large web gr...
Our work is motivated by the problem of ranking hyperlinked documents for a given query. Given an arbitrary directed graph with edge and node labels, we present a new flow-based ...