Personalizing web search results by reading level

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 r...

• Type: Journal
• Year: 2011
• Where: CIKM
• Authors: Kevyn Collins-Thompson, Paul N. Bennett, Ryen W. White, Sebastian de la Chica, David Sontag
• Keywords: CIKM 2011, Information Technology, Introductory Texts, Retrieval Models, Web Search Engines