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Probabilistic Topic Model for Hybrid Recommender Systems: A Stochastic Variational Bayesian Approach

Citations

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Cited by:

  1. Shimi Naurin Ahmad & Michel Laroche, 2023. "Extracting marketing information from product reviews: a comparative study of latent semantic analysis and probabilistic latent semantic analysis," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 662-676, December.
  2. Wang, Xin (Shane) & Ryoo, Jun Hyun (Joseph) & Bendle, Neil & Kopalle, Praveen K., 2021. "The role of machine learning analytics and metrics in retailing research," Journal of Retailing, Elsevier, vol. 97(4), pages 658-675.
  3. Marc R. Dotson & Joachim Büschken & Greg M. Allenby, 2020. "Explaining Preference Heterogeneity with Mixed Membership Modeling," Marketing Science, INFORMS, vol. 39(2), pages 407-426, March.
  4. Li, Jiawen & Meng, Lu & Zhang, Zelin & Yang, Kejia, 2023. "Low-frequency, high-impact: Discovering important rare events from UGC," Journal of Retailing and Consumer Services, Elsevier, vol. 70(C).
  5. Alex Burnap & John R. Hauser & Artem Timoshenko, 2019. "Product Aesthetic Design: A Machine Learning Augmentation," Papers 1907.07786, arXiv.org, revised Nov 2022.
  6. Scholdra, Thomas P. & Wichmann, Julian R.K. & Reinartz, Werner J., 2023. "Reimagining personalization in the physical store," Journal of Retailing, Elsevier, vol. 99(4), pages 563-579.
  7. Xu, Lu & Pan, Yuchen & Wu, Desheng & Olson, David L., 2025. "A novel O2O service recommendation method based on dynamic preference similarity," Omega, Elsevier, vol. 133(C).
  8. Jifeng Mu & Jonathan Z. Zhang, 2025. "Artificial intelligence marketing usage and firm performance," Journal of the Academy of Marketing Science, Springer, vol. 53(4), pages 1081-1134, July.
  9. Yu Fu & Michael Stanley Smith & Anastasios Panagiotelis, 2025. "Vector Copula Variational Inference and Dependent Block Posterior Approximations," Papers 2503.01072, arXiv.org, revised Oct 2025.
  10. Wang, Xue & Fan, Li-Wei & Zhang, Hongyan & Zhou, Peng, 2025. "Hydrogen fuel cell technology development in China: Technology evolution, city-cluster network and industry chain distribution," Energy, Elsevier, vol. 322(C).
  11. S. Abinaya & K. Indira & S. Karthiga & T. Rajasenbagam, 2023. "Time Cluster Personalized Ranking Recommender System in Multi-Cloud," Mathematics, MDPI, vol. 11(6), pages 1-17, March.
  12. Jiapeng Liu & Miłosz Kadziński & Xiuwu Liao, 2023. "Modeling Contingent Decision Behavior: A Bayesian Nonparametric Preference-Learning Approach," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 764-785, July.
  13. 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.
  14. Qian, Yang & Ling, Haifeng & Meng, Xiangrui & Jiang, Yuanchun & Chai, Yidong & Liu, Yezheng, 2024. "Voice of the Professional: Acquiring competitive intelligence from large-scale professional generated contents," Journal of Business Research, Elsevier, vol. 180(C).
  15. Ma, Liye & Sun, Baohong, 2020. "Machine learning and AI in marketing – Connecting computing power to human insights," International Journal of Research in Marketing, Elsevier, vol. 37(3), pages 481-504.
  16. Loaiza-Maya, Rubén & Smith, Michael Stanley & Nott, David J. & Danaher, Peter J., 2022. "Fast and accurate variational inference for models with many latent variables," Journal of Econometrics, Elsevier, vol. 230(2), pages 339-362.
  17. Zelin Zhang & Kejia Yang & Jonathan Z. Zhang & Robert W. Palmatier, 2023. "Uncovering Synergy and Dysergy in Consumer Reviews: A Machine Learning Approach," Management Science, INFORMS, vol. 69(4), pages 2339-2360, April.
  18. Bruno Jacobs & Dennis Fok & Bas Donkers, 2021. "Understanding Large-Scale Dynamic Purchase Behavior," Marketing Science, INFORMS, vol. 40(5), pages 844-870, September.
  19. Xiong, Yingqiu & Liu, Yezheng & Qian, Yang & Jiang, Yuanchun & Chai, Yidong & Ling, Haifeng, 2024. "Review-based recommendation under preference uncertainty: An asymmetric deep learning framework," European Journal of Operational Research, Elsevier, vol. 316(3), pages 1044-1057.
  20. Martin, Gael M. & Frazier, David T. & Maneesoonthorn, Worapree & Loaiza-Maya, Rubén & Huber, Florian & Koop, Gary & Maheu, John & Nibbering, Didier & Panagiotelis, Anastasios, 2024. "Bayesian forecasting in economics and finance: A modern review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 811-839.
  21. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
  22. W. Jason Choi & Qihong Liu & Jiwoong Shin, 2024. "Predictive Analytics and Ship-Then-Shop Subscription," Management Science, INFORMS, vol. 70(2), pages 1012-1028, February.
  23. Miikka Blomster & Timo Koivumäki, 2022. "Exploring the resources, competencies, and capabilities needed for successful machine learning projects in digital marketing," Information Systems and e-Business Management, Springer, vol. 20(1), pages 123-169, March.
  24. Lanfei Shi & Jin Liu & Yongjun Li & Natasha Zhang Foutz, 2025. "Ephemeral State-Dependent Recommendation for Digital Content," Information Systems Research, INFORMS, vol. 36(4), pages 2344-2357, December.
  25. 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).
  26. Yinxing Li & Nobuhiko Terui, 2026. "Social media effects on multi-generational diffusion of information technology products," Electronic Commerce Research, Springer, vol. 26(2), pages 1461-1488, April.
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