TY - GEN
T1 - Personalizing web search results by reading level
AU - Collins-Thompson, Kevyn
AU - Bennett, Paul N.
AU - White, Ryen W.
AU - De La Chica, Sebastian
AU - Sontag, David
PY - 2011
Y1 - 2011
N2 - 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 search engines do a poor job of serving an important segment of the population: children. While there are many important problems in interface design, content filtering, and results presentation related to addressing children's search needs, perhaps the most fundamental challenge is simply that of providing relevant results at the right level of reading difficulty. At the opposite end of the proficiency spectrum, it may also be valuable for technical users to find more advanced material or to filter out material at lower levels of difficulty, such as tutorials and introductory texts. We show how reading level can provide a valuable new relevance signal for both general and personalized Web search. We describe models and algorithms to address the three key problems in improving relevance for search using reading difficulty: estimating user proficiency, estimating result difficulty, and re-ranking based on the difference between user and result reading level profiles. We evaluate our methods on a large volume of Web query traffic and provide a large-scale log analysis that highlights the importance of finding results at an appropriate reading level for the user.
AB - 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 search engines do a poor job of serving an important segment of the population: children. While there are many important problems in interface design, content filtering, and results presentation related to addressing children's search needs, perhaps the most fundamental challenge is simply that of providing relevant results at the right level of reading difficulty. At the opposite end of the proficiency spectrum, it may also be valuable for technical users to find more advanced material or to filter out material at lower levels of difficulty, such as tutorials and introductory texts. We show how reading level can provide a valuable new relevance signal for both general and personalized Web search. We describe models and algorithms to address the three key problems in improving relevance for search using reading difficulty: estimating user proficiency, estimating result difficulty, and re-ranking based on the difference between user and result reading level profiles. We evaluate our methods on a large volume of Web query traffic and provide a large-scale log analysis that highlights the importance of finding results at an appropriate reading level for the user.
KW - personalization
KW - re-ranking
KW - reading difficulty
UR - http://www.scopus.com/inward/record.url?scp=83055181765&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=83055181765&partnerID=8YFLogxK
U2 - 10.1145/2063576.2063639
DO - 10.1145/2063576.2063639
M3 - Conference contribution
AN - SCOPUS:83055181765
SN - 9781450307178
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 403
EP - 412
BT - CIKM'11 - Proceedings of the 2011 ACM International Conference on Information and Knowledge Management
T2 - 20th ACM Conference on Information and Knowledge Management, CIKM'11
Y2 - 24 October 2011 through 28 October 2011
ER -