Recommendations Using Information from Multiple Association Rules: A Probabilistic Approach
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DOI: 10.1287/isre.2015.0583
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References listed on IDEAS
- Kartik Hosanagar & Daniel Fleder & Dokyun Lee & Andreas Buja, 2014. "Will the Global Village Fracture Into Tribes? Recommender Systems and Their Effects on Consumer Fragmentation," Management Science, INFORMS, vol. 60(4), pages 805-823, April.
- Ozlem Ergun & Gultekin Kuyzu & Martin Savelsbergh, 2007. "Reducing Truckload Transportation Costs Through Collaboration," Transportation Science, INFORMS, vol. 41(2), pages 206-221, May.
- Gediminas Adomavicius & Alexander Tuzhilin & Rong Zheng, 2011. "REQUEST: A Query Language for Customizing Recommendations," Information Systems Research, INFORMS, vol. 22(1), pages 99-117, March.
- Gerald Häubl & Valerie Trifts, 2000. "Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids," Marketing Science, INFORMS, vol. 19(1), pages 4-21, May.
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Cited by:
- Jiawei Chen & Yinghui (Catherine) Yang & Hongyan Liu, 2021. "Mining Bilateral Reviews for Online Transaction Prediction: A Relational Topic Modeling Approach," Information Systems Research, INFORMS, vol. 32(2), pages 541-560, June.
- Qian Wang & Jijun Yu & Weiwei Deng, 2019. "An adjustable re-ranking approach for improving the individual and aggregate diversities of product recommendations," Electronic Commerce Research, Springer, vol. 19(1), pages 59-79, March.
- Bae, Joonho & Park, Jinkyoo & Choi, Jeonghye & Bum Soh, Seung, 2023. "A recommending system for mobile games using the dynamic nonparametric model," Journal of Business Research, Elsevier, vol. 167(C).
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Keywords
personalization; Bayesian estimation; maximum likelihood; information theory; data analytics;All these keywords.
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