Paper Info
Title
Sogou-QCL: A New Dataset with Click Relevance Label.
Abstract
Data is of vital importance in the development of machine learning technologies. Recently, within the information retrieval field, a number of neural ranking frameworks have been proposed to address the ad-hoc search. These models usually need a large amount of query-document relevance judgments for training. However, obtaining this kind of relevance judgments needs a lot of money and manual effort. To shed light on this problem, researchers seek to use implicit feedback from users of search engines to improve the ranking performance. In this paper, we present a new dataset, Sogou-QCL, which contains 537,366 queries and five kinds of weak relevance labels for over 12 million query-document pairs. We apply Sogou-QCL dataset to train recent neural ranking models and show its potential to serve as weak supervision for ranking. We believe that Sogou-QCL will have a broad impact on corresponding areas.
Year
DOI
Venue
2018
10.1145/3209978.3210092
SIGIR
Keywords
Field
DocType
Test collection,document ranking,search evaluation
Search engine,Information retrieval,Ranking,Computer science
Conference
ISBN
Citations 
PageRank 
978-1-4503-5657-2
7
0.44
References 
Authors
15
6
Name
Order
Citations
PageRank
Yukun Zheng1314.02
Zhen Fan270.78
Yiqun Liu31592136.51
Cheng Luo49412.58
Min Zhang51658134.93
Shaoping Ma61544126.00