A query model based on normalized log-likelihood

Leveraging information from relevance assessments has been proposed as an effective means for improving retrieval. We introduce a novel language modeling method which uses information from each assessed document and their aggregate. While most previous approaches focus either on features of the entire set or on features of the individual relevant documents, our model exploits features of both the documents and the set as a whole. When evaluated, we show that our model is able to significantly improve over state-of-art feedback methods. Categories and Subject Descriptors H.3 [Information Storage and Retrieval]: H.3.1 Content Analysis and Indexing; H.3.3 Information Search and Retrieval—Retrieval Models General Terms Algorithms, Theory, Experimentation, Measurement Keywords Language modeling, Query models, Relevance feedback

• Type: Conference
• Year: 2009
• Where: CIKM
• Authors: Edgar Meij, Wouter Weerkamp, Maarten de Rijke
• Keywords: CIKM 2009, Database, Language Modeling Method, Model Exploits Features, State-of-art Feedback Methods