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Users of Web search engines reveal their information needs through queriesand clicks, making click logs a useful asset for information retrieval.However, click logs have not been publicly released for academic use, becausethey can be too revealing of personally or commercially sensitive information.This paper describes a click data release related to the TREC Deep LearningTrack document corpus. After aggregation and filtering, including a k-anonymityrequirement, we find 1.4 million of the TREC DL URLs have 18 millionconnections to 10 million distinct queries. Our dataset of these queries andconnections to TREC documents is of similar size to proprietary datasets usedin previous papers on query mining and ranking. We perform some preliminaryexperiments using the click data to augment the TREC DL training data, offeringby comparison: 28x more queries, with 49x more connections to 4.4x more URLs inthe corpus. We present a description of the dataset's generation process,characteristics, use in ranking and suggest other potential uses.

ORCAS: 18 Million Clicked Query-Document Pairs for Analyzing Search