Personalizing Web Search Results by Reading Level - Collins-Thompson, Kevyn; Bennett, Paul; White, Ryen W.; Chica, Sebastian de la; Sontag, David (Proceedings of the 20th ACM International Conference on Information and Knowledge Management (CIKM 2011), 2011)
Traditionally, search engines have ignored the reading difficulty of documents and the reading profi ciency 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 profi ciency spectrum, it may also be valuable for technical users to find more advanced material or to fi lter 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 profi ciency, estimating result diffi culty, and re-ranking based on the di fference between user and result reading level pro les. We evaluate our methods on a large volume of Web query trac and provide a large-scale log analysis that highlights the importance of fi nding results at an appropriate reading level for the user.