Advances in Information Retrieval

Volume 6611 of the series Lecture Notes in Computer Science pp 605-610

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

  • M-Dyaa AlbakourAffiliated withUniversity of Essex
  • , Udo KruschwitzAffiliated withUniversity of Essex
  • , Nikolaos NanasAffiliated withCentre for Research and Technology
  • , Yunhyong KimAffiliated withRobert Gordon University
  • , Dawei SongAffiliated withRobert Gordon University
  • , Maria FasliAffiliated withUniversity of Essex
  • , Anne De RoeckAffiliated withOpen University

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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.