Predicting query difficulty on the web by learning visual clues

@inproceedings{Jensen2005PredictingQD,
  title={Predicting query difficulty on the web by learning visual clues},
  author={Eric C. Jensen and Steven M. Beitzel and David A. Grossman and Ophir Frieder and Abdur Chowdhury},
  booktitle={SIGIR},
  year={2005}
}
We describe a method for predicting query difficulty in a precision-oriented web search task. Our approach uses visual features from retrieved surrogate document representations (titles, snippets, etc.) to predict retrieval effectiveness for a query. By training a supervised machine learning algorithm with manually evaluated queries, visual clues indicative of relevance are discovered. We show that this approach has a moderate correlation of 0.57 with precision at 10 scores from manual… CONTINUE READING
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