Efficiently Collecting Relevance Information from Clickthroughs for Web Retrieval System Evaluation
Jing He (peaceful (point) he <at> gmail (point) com)
Wednesday 7 December 2011 at 11:30 AM
Salle 3195, Pavillon André-Aisenstadt
Various click models have been recently proposed as a principled approach to infer the relevance of documents from the clickthrough data. The inferred document relevance is potentially useful in evaluating the Web retrieval systems. In practice, it generally requires to acquire the accurate evaluation results within minimal users' query submissions. This problem is important for speeding up search engine development and evaluation cycle and acquiring reliable evaluation results on tail queries. In this talk, I present a reordering framework for efficient evaluation problem in the context of clickthrough based Web retrieval evaluation. The main idea is to move up the documents that contribute more for the evaluation task. Both user study and TREC data based simulation experiments validate that the reordering framework results in much fewer query submissions to get accurate evaluation results with only a little harm to the users' utility.
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