Traditionally, search engines have ignored the reading di\u000efficulty of documents and the reading profi\fciency 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 \ffiltering, 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 di\u000efficulty. At the opposite end of the profi\fciency spectrum, it may also be valuable for technical users to \ffind more advanced material or to fi\flter out material at lower levels of di\u000efficulty, 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 diffi\u000eculty: estimating user profi\fciency, estimating result diffi\fculty, and re-ranking based on the di\u000bfference between user and result reading level pro\fles. We evaluate our methods on a large volume of Web query tra\u000ec and provide a large-scale log analysis that highlights the importance of fi\fnding results at an appropriate reading level for the user.<\/p>\n","protected":false},"excerpt":{"rendered":"
Traditionally, search engines have ignored the reading di\u000efficulty of documents and the reading profi\fciency 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. 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