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Erschienen in:
2011 | OriginalPaper | Buchkapitel
AutoEval: An Evaluation Methodology for Evaluating Query Suggestions Using Query Logs
verfasst von : M-Dyaa Albakour, Udo Kruschwitz, Nikolaos Nanas, Yunhyong Kim, Dawei Song, Maria Fasli, Anne De Roeck
Erschienen in: Advances in Information Retrieval
Verlag: Springer Berlin Heidelberg
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User evaluations of search engines are expensive and not easy to replicate. The problem is even more pronounced when assessing adaptive search systems, for example system-generated query modification suggestions that can be derived from past user interactions with a search engine. Automatically predicting the performance of different modification suggestion models
before
getting the users involved is therefore highly desirable.
AutoEval
is an evaluation methodology that assesses the quality of query modifications generated by a model using the query logs of past user interactions with the system. We present experimental results of applying this methodology to different adaptive algorithms which suggest that the predicted quality of different algorithms is in line with user assessments. This makes
AutoEval
a suitable evaluation framework for adaptive interactive search engines.