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AutoEval: An Evaluation Methodology for Evaluating Query Suggestions Using Query Logs

Albakour, M-Dyaa and Kruschwitz, Udo and Nanas, Nikolaos and Kim, Yunhyong and Song, Dawei and Fasli, Maria and De Roeck, Anne (2011) AutoEval: An Evaluation Methodology for Evaluating Query Suggestions Using Query Logs. In: UNSPECIFIED, ? - ?.

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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 or Workshop Item (UNSPECIFIED)
Additional Information: Published proceedings: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 14 Aug 2012 14:59
Last Modified: 23 Sep 2022 18:28
URI: http://repository.essex.ac.uk/id/eprint/3668

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