| How algorithmic confounding in recommendation systems increases homogeneity and decreases utility AJB Chaney, BM Stewart, BE Engelhardt Proceedings of the 12th ACM Conference on Recommender Systems, 2018 | 409 | 2018 |
| Visualizing topic models A Chaney, D Blei Proceedings of the International AAAI Conference on Web and Social Media 6 …, 2012 | 344 | 2012 |
| A probabilistic model for using social networks in personalized item recommendation AJB Chaney, DM Blei, T Eliassi-Rad Proceedings of the 9th ACM Conference on Recommender Systems, 43-50, 2015 | 223 | 2015 |
| How can machine learning aid behavioral marketing research? L Hagen, K Uetake, N Yang, B Bollinger, AJB Chaney, D Dzyabura, ... Marketing Letters 31, 361-370, 2020 | 58 | 2020 |
| A large-scale exploration of group viewing patterns AJB Chaney, M Gartrell, JM Hofman, J Guiver, N Koenigstein, P Kohli, ... Proceedings of the ACM International Conference on Interactive Experiences …, 2014 | 33 | 2014 |
| Simurec: Workshop on synthetic data and simulation methods for recommender systems research MD Ekstrand, A Chaney, P Castells, R Burke, D Rohde, M Slokom Proceedings of the 15th ACM conference on recommender systems, 803-805, 2021 | 21 | 2021 |
| “Dark patterns” in online services: a motivating study and agenda for future research J Runge, D Wentzel, JY Huh, A Chaney Marketing Letters 34 (1), 155-160, 2023 | 19 | 2023 |
| Detecting and characterizing events AJB Chaney, H Wallach, M Connelly, DM Blei Proceedings of the 2016 Conference on Empirical Methods in Natural Language …, 2016 | 18 | 2016 |
| Real-time topic models for crisis counseling K Dinakar, AJB Chaney, H Lieberman, DM Blei KDD DSSG Workshop, 1-4, 2014 | 18 | 2014 |
| Recommendation system simulations: A discussion of two key challenges AJB Chaney arXiv preprint arXiv:2109.02475, 2021 | 11 | 2021 |
| MORS 2021: 1st Workshop on Multi-Objective Recommender Systems H Abdollahpouri, M Elahi, M Mansoury, S Sahebi, Z Nazari, A Chaney, ... Proceedings of the 15th ACM Conference on Recommender Systems, 787-788, 2021 | 7 | 2021 |
| Mining large-scale tv group viewing patterns for group recommendation A Chaney, M Gartrell, J Hofman, J Guiver, N Koenigstein, P Kohli, ... Technical Report, MSR-TR-2013-114, Microsoft Research, Microsoft Corporation, 2013 | 7 | 2013 |
| Algorithmic recommendations have limited effects on polarization: a naturalistic experiment on YouTube N Liu, MA Baum, AJ Berinsky, AJ Chaney, J Benedictis-Kessner, A Guess, ... September 18, 398-404, 2023 | 4 | 2023 |
| Poisson trust factorization for incorporating social networks into personalized item recommendation AJB Chaney, P Gopalan, D Blei NIPS Workshop: What Difference Does Personalization Make, 2013 | 3 | 2013 |
| Short-term exposure to filter-bubble recommendation systems has limited polarization effects: Naturalistic experiments on YouTube N Liu, XE Hu, Y Savas, MA Baum, AJ Berinsky, AJB Chaney, C Lucas, ... Proceedings of the National Academy of Sciences 122 (8), e2318127122, 2025 | 1 | 2025 |
| A guide to black box variational inference for gamma distributions AJ Chaney | 1 | 2015 |
| Who, what, when, where, and why? A computational approach to understanding historical events using state depart-ment cables A Chaney, H Wallach, D Blei Unpublished Working Paper, 2015 | 1 | 2015 |
| Do Online Video Recommendation Algorithms Increase Polarization? BM Stewart, N Liu, M Baum, AJ Berinsky, AJB Chaney, ... OSF, 2024 | | 2024 |
| Amended: Do Online Video Recommendation Algorithms Increase Polarization? N Liu, J Anastasopoulos, M Baum, AJ Berinsky, AJB Chaney, ... OSF, 2021 | | 2021 |
| Nonparametric Deconvolution Models AJB Chaney, A Verma, Y Lee, BE Engelhardt arXiv preprint arXiv:2003.07718, 2020 | | 2020 |
David BleiProfessor of Statistics and Computer Science, Columbia UniversityE-mailová adresa ověřena na: columbia.edu
