AutoEval: an evaluation methodology for evaluating query suggestions using query logs

Albakour, M.-D., Kruschwitz, U., Nanas, N., Kim, Y. , Song, D., Fasli, M. and De Roeck, A. (2011) AutoEval: an evaluation methodology for evaluating query suggestions using query logs. In: 33rd European Conference on Advances in Information Retrieval (ECIR'11), Dublin, Ireland, 18-21 April 2011, pp. 605-610.

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Publisher's URL: http://dl.acm.org/citation.cfm?id=1996889.1996966

Abstract

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.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Kim, Dr Yunhyong
Authors: Albakour, M.-D., Kruschwitz, U., Nanas, N., Kim, Y., Song, D., Fasli, M., and De Roeck, A.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
College/School:College of Arts > School of Humanities > Information Studies
Publisher:Springer-Verlag

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