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Predicting ad click-through rates via feature-based fully coupled interaction tensor factorization

Crossref DOI link: https://doi.org/10.1016/j.elerap.2016.01.004

Published Print: 2016-03

Update policy: https://doi.org/10.1016/elsevier_cm_policy

Authors

Shan, Lili

Lin, Lei

Sun, Chengjie

Wang, Xiaolong
Funding

Funding for this research was provided by:

National Natural Science Foundation of China (61300114)

National Natural Science Foundation of China (61572151)

Specialized Research Fund (20132302120047)

China Postdoctoral Science Special Foundation (2014T70340)
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Text and Data Mining valid from 2016-03-01
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This article is maintained by: Elsevier

Article Title: Predicting ad click-through rates via feature-based fully coupled interaction tensor factorization

Journal Title: Electronic Commerce Research and Applications

CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.elerap.2016.01.004

Content Type: article

Copyright: © 2016 Elsevier B.V. All rights reserved.