The DOI matches its Crossref record; 42 of the 43 references it cites were found in a registry or an index.
Optimal pricing in e-commerce based on sparse and noisy data
Overview
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Claims the paper makes
Citations
Cites
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Optimal pricing for new and remanufactured products
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Cited by
A strategic framework for artificial intelligence in marketing
Huang, M.-H., & Rust, R. T. (2021) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-020-00749-9 · Cited by 2,059
“Bauer and Jannach (2018) show that a machine-learning framework based on Bayesian inference can optimize online pricing even when data are updated frequently, and are sparse and noisy.”
BackgroundArtificial intelligence in marketing: Systematic review and future research direction
Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2020.100002 · Cited by 757
Artificial intelligence in E-Commerce: a bibliometric study and literature review
Bawack, R. E., Wamba, S. F., Carillo, K. D. A., & Akter, S. (2022) · Electronic Markets · doi:10.1007/s12525-022-00537-z · Cited by 372
Strategic Framework for Leveraging Artificial Intelligence in Future Marketing Decision-Making
Hicham, N., Nassera, H., & Karim, S. (2023) · Journal of Intelligent Management Decision · doi:10.56578/jimd020304 · Cited by 155
A framework for understanding artificial intelligence research: insights from practice
Bawack, R. E., Wamba, S. F., & Carillo, K. (2021) · Journal of Enterprise Information Management · doi:10.1108/jeim-07-2020-0284 · Cited by 137
Metadata
- Authors
- Bauer, J., & Jannach, D.
- Affiliations
- TU Dortmund University (DE)
- University of Klagenfurt (AT)
- Corresponding author
- Bauer, Josef
- ORCID
- Jannach, Dietmar
- Venue
- Decision Support Systems
- Publisher
- Elsevier BV
- Published
- 2018-02
- Volume
- 106
- Pages
- 53-63
- ISSN
- 0167-9236 (ISSN-L)
- OpenAlex
- W2775564956
- Cited by
- 92
- References
- 43
- Citations by year
- 2026: 7 · 2025: 7 · 2024: 19 · 2023: 24 · 2022: 10 · 2021: 14 · 2020: 5 · 2019: 4 · 2018: 2
- FWCI
- 5.14
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- Decision Support Systems (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Supply Chain and Inventory Management
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
- Social Sciences › Decision Sciences › Management Science and Operations Research › Auction Theory and Applications
- Keywords
- dynamic pricing, optimal pricing, e-commerce, data sparsity, noisy data, Bayesian inference, kernel regression, revenue maximization, profit maximization
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Optimal pricing in e-commerce based on sparse and noisy data. https://app.cochat.ai/papers/10.1016/j.dss.2017.12.002
- The paper
- Bauer, J., & Jannach, D. (2018). Optimal pricing in e-commerce based on sparse and noisy data. Decision Support Systems. https://doi.org/10.1016/j.dss.2017.12.002
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
