Artificial intelligence in E-Commerce: a bibliometric study and literature review-Reference-Cited by-同舟云学术

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Artificial intelligence in E-Commerce: a bibliometric study and literature review

Published:2022-03 Issue:1 Volume:32 Page:297-338
ISSN:1019-6781
Container-title:Electronic Markets
language:en
Short-container-title:Electron Markets

Author:

Bawack Ransome Epie,Wamba Samuel Fosso,Carillo Kevin Daniel André,Akter Shahriar

Publisher

Springer Science and Business Media LLC

Subject

Management of Technology and Innovation,Marketing,Computer Science Applications,Economics and Econometrics,Business and International Management

Link

https://link.springer.com/content/pdf/10.1007/s12525-022-00537-z.pdf

Reference409 articles.

1. Abbasi, A., Zhang, Z., Zimbra, D., Chen, H., & Nunamaker, J. F. (2010). Detecting fake websites: The contribution of statistical learning theory. MIS Quarterly, 34(3), 435–461. https://doi.org/10.2307/25750686

2. Abdel-Karim, B. M., Pfeuffer, N., & Hinz, O. (2021). Machine learning in information systems - a bibliographic review and open research issues. Electronic Markets, 31(3), 643–670. https://doi.org/10.1007/s12525-021-00459-2

3. Adomavicius, G., Bockstedt, J. C., Curley, S. P., & Zhang, J. (2013). Do Recommender Systems Manipulate Consumer Preferences? A Study of Anchoring Effects. Information Systems Research, 24(4), 956–975. https://doi.org/10.1057/isre.2013.0497

4. Adomavicius, G., Sankaranarayanan, R., Sen, S., & Tuzhilin, A. (2005). Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems, 23(1), 103–145. https://doi.org/10.1145/1055709.1055714

5. Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749. https://doi.org/10.1109/TKDE.2005.99

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