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

Albakour, M-Dyaa; Kruschwitz, Udo; Nanas, Nikolaos; Kim, Yunhyong; Song, Dawei; Fasli, Maria and De Roeck, Anne (2011). AutoEval: an evaluation methodology for evaluating query suggestions using query logs. In: 33rd European Conference on Information Retrieval (ECIR2011), 19-21 Apr 2011, Dublin, Ireland, Springer-Verlag, pp. 605–610.

DOI: https://doi.org/10.1007/978-3-642-20161-5_60

URL: http://www.springerlink.com/content/w0978015n1622h...

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.

Viewing alternatives

Metrics

Item Actions

Export

You can export this page using these formats Digital Object Identifier - DOI