AutoEval: An Evaluation Methodology for Evaluating Query Suggestions Using Query Logs
AutoEval: An Evaluation Methodology for Evaluating Query Suggestions Using Query Logs
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AutoEval：一种使用查询日志评估查询建议的评估方法
DOI：
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发表时间：
2011
期刊：
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通讯作者：
A. Roeck
中科院分区：
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作者：
M. Albakour;Udo Kruschwitz;Nikolaos Nanas;Yunhyong Kim;D. Song;Maria Fasli;A. Roeck
用户对搜索引擎的评价是昂贵的，而且不容易复制。在评估自适应搜索系统时，这个问题更加明显，例如，系统生成的查询修改建议可以从过去用户与搜索引擎的交互中得到。因此，在用户参与之前自动预测不同修改建议模型的性能是非常可取的。AutoEval是一种评估方法，它使用过去用户与系统交互的查询日志来评估由模型生成的查询修改的质量。我们提出了将该方法应用于不同自适应算法的实验结果，表明不同算法的预测质量符合用户评估。这使得AutoEval成为一个适合自适应交互式搜索引擎的评估框架
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