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Optimal pricing in e-commerce based on sparse and noisy data
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“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
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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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