Search Personalization Using Machine Learning

Citations

Cited by:

1. Omid Rafieian, 2023. "Optimizing User Engagement Through Adaptive Ad Sequencing," Marketing Science, INFORMS, vol. 42(5), pages 910-933, September.
2. Cloarec, Julien, 2020. "The personalization–privacy paradox in the attention economy," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
3. Herhausen, Dennis & Bernritter, Stefan F. & Ngai, Eric W.T. & Kumar, Ajay & Delen, Dursun, 2024. "Machine learning in marketing: Recent progress and future research directions," Journal of Business Research, Elsevier, vol. 170(C).
4. Hema Yoganarasimhan & Ebrahim Barzegary & Abhishek Pani, 2023. "Design and Evaluation of Optimal Free Trials," Management Science, INFORMS, vol. 69(6), pages 3220-3240, June.
5. Reuter-Oppermann, Melanie & Wolff, Clemens & Pumplun, Luisa, 2021. "Next Frontiers in Emergency Medical Services in Germany: Identifying Gaps between Academia and Practice," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 124665, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
6. Tino Werner, 2023. "Quantitative robustness of instance ranking problems," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(2), pages 335-368, April.
7. Omid Rafieian & Hema Yoganarasimhan, 2021. "Targeting and Privacy in Mobile Advertising," Marketing Science, INFORMS, vol. 40(2), pages 193-218, March.
8. Florian Peiseler & Alexander Rasch & Shiva Shekhar, 2022. "Imperfect information, algorithmic price discrimination, and collusion," Scandinavian Journal of Economics, Wiley Blackwell, vol. 124(2), pages 516-549, April.
9. van Giffen, Benjamin & Herhausen, Dennis & Fahse, Tobias, 2022. "Overcoming the pitfalls and perils of algorithms: A classification of machine learning biases and mitigation methods," Journal of Business Research, Elsevier, vol. 144(C), pages 93-106.
10. Hema Yoganarasimhan & Ebrahim Barzegary & Abhishek Pani, 2020. "Design and Evaluation of Personalized Free Trials," Papers 2006.13420, arXiv.org.
11. Ali Goli & Anja Lambrecht & Hema Yoganarasimhan, 2024. "A Bias Correction Approach for Interference in Ranking Experiments," Marketing Science, INFORMS, vol. 43(3), pages 590-614, May.
12. Bergemann, Dirk & Ottaviani, Marco, 2021. "Information Markets and Nonmarkets," CEPR Discussion Papers 16459, C.E.P.R. Discussion Papers.
    • Dirk Bergemann & Marco Ottaviani, 2021. "Information Markets and Nonmarkets," Cowles Foundation Discussion Papers 2296, Cowles Foundation for Research in Economics, Yale University.
13. Zhang, Ruchuan & Gao, Weiyan & Chen, Shanshan & Zhou, Li & Li, Aijun, 2024. "Dose digital transformation contribute to improving financing efficiency? Evidence and implications for energy enterprises in China," Energy, Elsevier, vol. 300(C).
14. Hanyao Gao & Gang Kou & Haiming Liang & Hengjie Zhang & Xiangrui Chao & Cong-Cong Li & Yucheng Dong, 2024. "Machine learning in business and finance: a literature review and research opportunities," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-35, December.
15. Chenshuo Sun & Panagiotis Adamopoulos & Anindya Ghose & Xueming Luo, 2022. "Predicting Stages in Omnichannel Path to Purchase: A Deep Learning Model," Information Systems Research, INFORMS, vol. 33(2), pages 429-445, June.
16. Alantari, Huwail J. & Currim, Imran S. & Deng, Yiting & Singh, Sameer, 2022. "An empirical comparison of machine learning methods for text-based sentiment analysis of online consumer reviews," International Journal of Research in Marketing, Elsevier, vol. 39(1), pages 1-19.
17. Brei, Vinicius Andrade, 2020. "Machine Learning in Marketing: Overview, Learning Strategies, Applications, and Future Developments," Foundations and Trends(R) in Marketing, now publishers, vol. 14(3), pages 173-236, August.
18. Tino Werner, 2022. "Elicitability of Instance and Object Ranking," Decision Analysis, INFORMS, vol. 19(2), pages 123-140, June.
19. Tsan‐Ming Choi & Subodha Kumar & Xiaohang Yue & Hau‐Ling Chan, 2022. "Disruptive Technologies and Operations Management in the Industry 4.0 Era and Beyond," Production and Operations Management, Production and Operations Management Society, vol. 31(1), pages 9-31, January.
20. Ludovica Cesareo & Claudia Townsend & Eugene Pavlov, 2023. "Hideous but worth it: Distinctive ugliness as a signal of luxury," Journal of the Academy of Marketing Science, Springer, vol. 51(3), pages 636-657, May.
21. Schaefer, Maximilian & Sapi, Geza, 2023. "Complementarities in learning from data: Insights from general search," Information Economics and Policy, Elsevier, vol. 65(C).
    • Maximilian Schäfer & Geza Sapi, 2023. "Complementarities in learning from data: insights from general search," Post-Print hal-04261926, HAL.
22. Shengjun Mao & Sanjeev Dewan & Yi-Jen (Ian) Ho, 2023. "Personalized Ranking at a Mobile App Distribution Platform," Information Systems Research, INFORMS, vol. 34(3), pages 811-827, September.
23. Lohmann, Paul M & Gsottbauer, Elisabeth & Farrington, James & Human, Steve & Reisch, Lucia A, 2024. "Choice architecture promotes sustainable choices in online food-delivery apps," LSE Research Online Documents on Economics 125835, London School of Economics and Political Science, LSE Library.
24. Josué Martínez-Garmendia, 2024. "Machine learning for product choice prediction," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(3), pages 656-667, September.
25. Jens Foerderer, 2023. "Should we trust web-scraped data?," Papers 2308.02231, arXiv.org.