Predicting User Preferences
نویسنده
چکیده
The many metrics employed for the evaluation of search engine results have not themselves been conclusively evaluated. We propose a new measure for a metric’s ability to identify user preference of result lists. Using this measure, we evaluate the metrics Discounted Cumulated Gain, Mean Average Precision and classical precision, finding that the former performs best. We also show that considering more results for a given query can impair rather than improve a metric’s ability to predict user preferences.
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عنوان ژورنال:
- CoRR
دوره abs/1103.2886 شماره
صفحات -
تاریخ انتشار 2011