We consider a network of autonomous peers forming a logically global but physically distributed search engine, where every peer has its own local collection generated by independe...
Josiane Xavier Parreira, Sebastian Michel, Gerhard...
This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. We observe that users searching the web often perform ...
This work presents a study to bridge topic modeling and personalized search. A probabilistic topic model is used to extract topics from user search history. These topics can be se...
We present a user interface, the OntoRefiner1 system, for helping the user to navigate numerous retrieved documents after a search querying a semantic portal which integrates a ver...
Understanding the intent underlying user queries may help personalize search results and improve user satisfaction. In this paper, we develop a methodology for using ad clickthroug...
Azin Ashkan, Charles L. A. Clarke, Eugene Agichtei...