NDLI: Active Learning for Web Search Ranking via Noise Injection

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Active Learning for Web Search Ranking via Noise Injection

Content Provider	ACM Digital Library
Author	Zhang, Ya Cai, Wenbin Zhang, Muhan
Copyright Year	2015
Abstract	Learning to rank has become increasingly important for many information retrieval applications. To reduce the labeling cost at training data preparation, many active sampling algorithms have been proposed. In this article, we propose a novel active learning-for-ranking strategy called ranking-based sensitivity sampling (RSS), which is tailored for Gradient Boosting Decision Tree (GBDT), a machine-learned ranking method widely used in practice by major commercial search engines for ranking. We leverage the property of GBDT that samples close to the decision boundary tend to be sensitive to perturbations and design the active learning strategy accordingly. We further theoretically analyze the proposed strategy by exploring the connection between the sensitivity used for sample selection and model regularization to provide a potentially theoretical guarantee w.r.t. the generalization capability. Considering that the performance metrics of ranking overweight the top-ranked items, item rank is incorporated into the selection function. In addition, we generalize the proposed technique to several other base learners to show its potential applicability in a wide variety of applications. Substantial experimental results on both the benchmark dataset and a real-world dataset have demonstrated that our proposed active learning strategy is highly effective in selecting the most informative examples.
Starting Page	1
Ending Page	31
Page Count	31
File Format	PDF
ISSN	15591131
e-ISSN	1559114X
DOI	10.1145/2697391
Volume Number	9
Issue Number	1
Journal	ACM Transactions on the Web (TWEB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-01-23
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Active learning Noise injection Ranking Sensitivity sampling
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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