The quality of document content, which is an issue that is usually ignored for the traditional ad hoc retrieval task, is a critical issue for Web search. Web pages have a huge variation in quality relative to, for example, newswire articles. To address this problem, we propose a document quality language model approach that is incorporated into the basic query likelihood retrieval model in the form of a prior probability. Our results demonstrate that, on average, the new model is significantly

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... etter than the baseline (query likelihood model) in terms of MRR and precision at the top ranks. We also give a detailed query analysis which provides some interesting insights on the limitations of the quality model and the relationship between document quality and relevance.