Neural information retrieval: at the end of the early years-Reference-Cited by-同舟云学术

Neural information retrieval: at the end of the early years

Published:2017-11-10 Issue:2-3 Volume:21 Page:111-182
ISSN:1386-4564
Container-title:Information Retrieval Journal
language:en
Short-container-title:Inf Retrieval J

Author:

Onal Kezban Dilek,Zhang Ye,Altingovde Ismail Sengor,Rahman Md Mustafizur,Karagoz Pinar,Braylan Alex,Dang Brandon,Chang Heng-Lu,Kim Henna,McNamara Quinten,Angert Aaron,Banner Edward,Khetan Vivek,McDonnell Tyler,Nguyen An Thanh,Xu Dan,Wallace Byron C.,de Rijke Maarten,Lease Matthew

Funder

Ahold Delhaize

Amsterdam Data Science

Blendle

Bloomberg Research Grant program

Dutch national program COMMIT

Elsevier

European Community’s Seventh Framework Programme

ESF Research Network Program ELIAS

Royal Dutch Academy of Sciences (KNAW) under the Elite Network Shifts project

Microsoft Research Ph.D. program

Netherlands eScience Center

Netherlands Institute for Sound and Vision

Netherlands Organisation for Scientific Research (NWO)

Yahoo! Faculty Research and Engagement Program

Yandex

Türkiye Bilimsel ve Teknolojik Arastirma Kurumu

Türkiye Bilimler Akademisi (TR)

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Information Systems

Link

http://link.springer.com/content/pdf/10.1007/s10791-017-9321-y.pdf

Reference222 articles.

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2. Ai, Q., Yang, L., Guo, J., & Croft, W. B. (2016b). Improving language estimation with the paragraph vector model for ad-hoc retrieval. In Proceedings of the 39th international ACM SIGIR conference on research and development in information retrieval, ACM, New York, NY, USA, SIGIR’16 (pp. 869–872).

3. ALMasri, M., Berrut, C., & Chevallet, J. P. (2016). A comparison of deep learning based query expansion with pseudo-relevance feedback and mutual information. In European conference on information retrieval (pp. 709–715). Springer.

4. Amati, G., & Van Rijsbergen, C. J. (2002). Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM Transactions on Information Systems (TOIS), 20(4), 357–389.

5. Amer, N. O., Mulhem, P., & Gery, M. (2016). Toward word embedding for personalized information retrieval. In ACM SIGIR Workshop on Neural Information Retrieval (Neu-IR).

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