Generation of Synthetic Query Auto Completion Logs.
LECTURE NOTES IN COMPUTER SCIENCE 2020. [PMCID:
PMC7148242 DOI:
10.1007/978-3-030-45439-5_41]
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Abstract
Privacy concerns can prohibit research access to large-scale commercial query logs. Here we focus on generation of a synthetic log from a publicly available dataset, suitable for evaluation of query auto completion (QAC) systems. The synthetic log contains plausible string sequences reflecting how users enter their queries in a QAC interface. Properties that would influence experimental outcomes are compared between a synthetic log and a real QAC log through a set of side-by-side experiments, and confirm the applicability of the generated log for benchmarking the performance of QAC methods.
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