We formulate and study search algorithms that consider a user’s prior interactions with a wide variety of content to personalize that user’s current Web search. Rather than re...
Today’s search engines retrieve tens of thousands of web pages in response to fairly simple query articulations. These pages are retrieved on the basis of the query terms occurr...
Richong Zhang, Michael A. Shepherd, Jack Duffy, Ca...
Traditionally, search engines have ignored the reading difficulty of documents and the reading proficiency of users in computing a document ranking. This is one reason why Web se...
Kevyn Collins-Thompson, Paul N. Bennett, Ryen W. W...
Manually querying search engines in order to accumulate a large body of factual information is a tedious, error-prone process of piecemeal search. Search engines retrieve and rank...
Oren Etzioni, Michael J. Cafarella, Doug Downey, S...
Abstract: In this paper, we revisit our approach to construction of semanticlinguistic Feature Vectors (FVs) used to enhance Web search. These FVs are built based on domain semanti...