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Technical Model / Design Science⚓︎

On Design Science⚓︎

  • Hevner, Alan R., et al. "Design science in information systems research." MIS quarterly (2004): 75-105. source
  • Peffers, Ken, et al. "A design science research methodology for information systems research." Journal of management information systems 24.3 (2007): 45-77. source
  • Hevner, Alan, et al. "Design science research in information systems." Design research in information systems: theory and practice (2010): 9-22. source
  • Sein, Maung K., et al. "Action design research." MIS quarterly (2011): 37-56. source
  • Gregor, Shirley and Hevner, Alan R.. 2013. "Positioning and Presenting Design Science Research for Maximum Impact," MIS Quarterly, (37: 2) pp.337-355. source
  • Deng, Qi and Ji, Shaobo (2018) "A Review of Design Science Research in Information Systems: Concept, Process, Outcome, and Evaluation," Pacific Asia Journal of the Association for Information Systems: Vol. 10: Iss. 1, Article 2. source
  • Baskerville, Richard, et al. "Design science research contributions: Finding a balance between artifact and theory." Journal of the Association for Information Systems 19.5 (2018): 3. source
  • Maedche, Alexander, et al. "Conceptualization of the problem space in design science research." International conference on design science research in information systems and technology. Springer, Cham, 2019. source
  • Brendel, A. B., & Muntermann, J. (2022). Replication of design theories: Reflections on function, outcome, and impact. Information Systems Journal, 1– 19. source
  • Nagle, T., Doyle, C., Alhassan, I. M., & Sammon, D. (2022). The Research Method we Need or Deserve? A Literature Review of the Design Science Research Landscape. Communications of the Association for Information Systems, 50, pp-pp. source

Artificial Intelligence⚓︎

  • Nguyen, Q. N., Sidorova, A., & Torres, R. (2022). Artificial Intelligence in Business: A Literature Review and Research Agenda. Communications of the Association for Information Systems, 50, pp-pp. source

Deep Learning⚓︎

  • Luyang Chen, Markus Pelger, Jason Zhu (2023) Deep Learning in Asset Pricing. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4695
  • Samtani, S., Zhu, H., Padmanabhan, B., Chai, Y., & Chen, H. (2023). Deep learning for information systems research. Journal of Management Information Systems. https://doi.org/10.1080/07421222.2023.2172772

Reinforcement Learning⚓︎

  • Kaelbling, Leslie Pack, Michael L. Littman, and Andrew W. Moore. "Reinforcement learning: A survey." Journal of artificial intelligence research 4 (1996): 237-285. source
  • Arulkumaran, Kai, et al. "Deep reinforcement learning: A brief survey." IEEE Signal Processing Magazine 34.6 (2017): 26-38. source
  • Li, Yuxi. "Deep reinforcement learning." arXiv preprint arXiv:1810.06339 (2018). source
  • Li, Yuxi. "Reinforcement learning applications." arXiv preprint arXiv:1908.06973 (2019). source
  • Liebman, Elad, Maytal Saar-Tsechansky, and Peter Stone. "The Right Music at the Right Time: Adaptive Personalized Playlists Based on Sequence Modeling." MIS Quarterly 43.3 (2019). source
  • Wang, Hao-nan, et al. "Deep reinforcement learning: a survey." Frontiers of Information Technology & Electronic Engineering (2020): 1-19. source
  • Parker-Holder, Jack, et al. "Automated Reinforcement Learning (AutoRL): A Survey and Open Problems." arXiv preprint arXiv:2201.03916 (2022). source
  • Mark Sellke, Aleksandrs Slikvins (2022) The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity. Operations Research 0(0). source
  • Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu (2023) Nonstationary Reinforcement Learning: The Blessing of (More) Optimism. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4704
  • Yi Zhu, Jing Dong, Henry Lam (2023) Uncertainty Quantification and Exploration for Reinforcement Learning. Operations Research 0(0). https://doi.org/10.1287/opre.2023.2436

Self-Supervised Learning⚓︎

  • Jing, Longlong, and Yingli Tian. "Self-supervised visual feature learning with deep neural networks: A survey." IEEE transactions on pattern analysis and machine intelligence 43.11 (2020): 4037-4058. source
  • Xie, Yaochen, et al. "Self-supervised learning of graph neural networks: A unified review." arXiv preprint arXiv:2102.10757 (2021). source
  • Liu, Yixin, et al. "Graph self-supervised learning: A survey." arXiv preprint arXiv:2103.00111 (2021). source
  • Jaiswal, Ashish, et al. "A survey on contrastive self-supervised learning." Technologies 9.1 (2021): 2. source
  • Liu, Xiao, et al. "Self-supervised learning: Generative or contrastive." IEEE Transactions on Knowledge and Data Engineering (2021). source

Transfer Learning⚓︎

  • Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359. source
  • Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. "A survey of transfer learning." Journal of Big data 3.1 (2016): 1-40. source
  • Tan, Chuanqi, et al. "A survey on deep transfer learning." International conference on artificial neural networks. Springer, Cham, 2018. source
  • Zhuang, Fuzhen, et al. "A comprehensive survey on transfer learning." Proceedings of the IEEE 109.1 (2020): 43-76. source

Differential Privacy⚓︎

  • Dwork, Cynthia, et al. "Calibrating noise to sensitivity in private data analysis." Theory of cryptography conference. Springer, Berlin, Heidelberg, 2006. source
  • Zheng, Qinqing, et al. "Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion." arXiv preprint arXiv:2003.04493 (2020). source
  • Goodfellow, Ian. "Efficient per-example gradient computations." arXiv preprint arXiv:1510.01799 (2015). source
  • Abadi, Martin, et al. "Deep learning with differential privacy." Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. 2016. source
  • Mironov, Ilya. "Rényi differential privacy." 2017 IEEE 30th Computer Security Foundations Symposium (CSF). IEEE, 2017. source
  • McMahan, H. Brendan, et al. "A general approach to adding differential privacy to iterative training procedures." arXiv preprint arXiv:1812.06210 (2018). source
  • Mironov, Ilya, Kunal Talwar, and Li Zhang. "Rényi Differential Privacy of the Sampled Gaussian Mechanism." arXiv preprint arXiv:1908.10530 (2019). source
  • Dwork, Cynthia, and Aaron Roth. "The algorithmic foundations of differential privacy." Foundations and Trends in Theoretical Computer Science 9.3-4 (2014): 211-407. source
  • Dwork, Cynthia, and Adam Smith. "Differential privacy for statistics: What we know and what we want to learn." Journal of Privacy and Confidentiality 1.2 (2010). source
  • Ji, Zhanglong, Zachary C. Lipton, and Charles Elkan. "Differential privacy and machine learning: a survey and review." arXiv preprint arXiv:1412.7584 (2014). source
  • Jiang, Honglu, et al. "Differential Privacy and Its Applications in Social Network Analysis: A Survey." arXiv preprint arXiv:2010.02973 (2020). source
  • Yang, Mengmeng, et al. "Local differential privacy and its applications: A comprehensive survey." arXiv preprint arXiv:2008.03686 (2020). source

Explainable ML / DL / AI⚓︎

  • Angelino, Elaine, et al. "Learning certifiably optimal rule lists for categorical data." arXiv preprint arXiv:1704.01701 (2017). source
  • Lundberg, Scott M., and Su-In Lee. "A unified approach to interpreting model predictions." Advances in neural information processing systems 30 (2017). source
  • Lipton, Zachary C. "The mythos of model interpretability." Queue 16.3 (2018): 31-57. source
  • Lundberg, Scott M., et al. "From local explanations to global understanding with explainable AI for trees." Nature machine intelligence 2.1 (2020): 56-67. source
  • Molnar, Christoph. Interpretable machine learning. 2020. source
  • Arrieta, Alejandro Barredo, et al. "Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI." Information Fusion 58 (2020): 82-115. source
  • Wang, Zhuo, et al. "Scalable Rule-Based Representation Learning for Interpretable Classification." arXiv preprint arXiv:2109.15103 (2021). source
  • Chen, Valerie, et al. "Interpretable machine learning: Moving from mythos to diagnostics." Communications of the ACM, August 2022, Vol. 65 No. 8, Pages 43-50. source

Fairness⚓︎

  • Aumüller, Martin, Rasmus Pagh, and Francesco Silvestri. "Fair near neighbor search: Independent range sampling in high dimensions." Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems. 2020. source
  • Krakovsky, Marina. "Formalizing Fairness." Communications of the ACM, August 2022, Vol. 65 No. 8, Pages 11-13. source
  • Dong, Yushun, et al. "Fairness in Graph Mining: A Survey." arXiv preprint arXiv:2204.09888 (2022). source

Active Learning⚓︎

  • Aggarwal, C. C., Kong, X., Gu, Q., Han, J., & Yu, P. S. (2014). "Active learning: A survey". In Data Classification: Algorithms and Applications (pp. 571-605). CRC Press. source
  • Ren, Pengzhen, et al. "A Survey of Deep Active Learning." ArXiv:2009.00236 [Cs, Stat], Aug. 2020. arXiv.org. source
  • Atahan, Pelin, and Sumit Sarkar. "Accelerated learning of user profiles." Management Science 57.2 (2011): 215-239. source

Label Imbalance⚓︎

  • Nasir, Murtaza, et al. "Improving Imbalanced Machine Learning with Neighborhood-Informed Synthetic Sample Placement." Journal of Management Information Systems 39.4 (2022): 1116-1145. https://doi.org/10.1080/07421222.2022.2127453

Label Noise⚓︎

  • Han, Bo, et al. "A survey of label-noise representation learning: Past, present and future." arXiv preprint arXiv:2011.04406 (2020). source

Natural Language Processing⚓︎

Text Summarization⚓︎

  • Rush, Alexander M., Sumit Chopra, and Jason Weston. "A neural attention model for abstractive sentence summarization." arXiv preprint arXiv:1509.00685 (2015). source
  • Chen, Yen-Chun, and Mohit Bansal. "Fast abstractive summarization with reinforce-selected sentence rewriting." arXiv preprint arXiv:1805.11080 (2018). source
  • Gehrmann, Sebastian, Yuntian Deng, and Alexander M. Rush. "Bottom-up abstractive summarization." arXiv preprint arXiv:1808.10792 (2018). source

Topic Modeling⚓︎

  • Jelodar, Hamed, et al. "Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey." Multimedia Tools and Applications 78.11 (2019): 15169-15211. source
  • Qiang, Jipeng, et al. "Short text topic modeling techniques, applications, and performance: a survey." IEEE Transactions on Knowledge and Data Engineering (2020). source
  • Vayansky, Ike, and Sathish AP Kumar. "A review of topic modeling methods." Information Systems 94 (2020): 101582. source
  • Kherwa, Pooja, and Poonam Bansal. "Topic modeling: a comprehensive review." EAI Endorsed transactions on scalable information systems 7.24 (2020). source
  • Chauhan, Uttam, and Apurva Shah. "Topic Modeling Using Latent Dirichlet allocation: A Survey." ACM Computing Surveys (CSUR) 54.7 (2021): 1-35. source
  • Yi Yang, Kunpeng Zhang, Yangyang Fan (2022) sDTM: A Supervised Bayesian Deep Topic Model for Text Analytics. Information Systems Research 0(0). source
  • Li, Weifeng and Chen, Hsinchun. 2022. "Discovering Emerging Threats in the Hacker Community: A Nonparametric Emerging Topic Detection Framework," MIS Quarterly, (46: 4) pp.2337-2350. source

Personalized Feedback⚓︎

  • Jiyeon Hong, Paul R. Hoban (2022) Writing More Compelling Creative Appeals: A Deep Learning-Based Approach. Marketing Science 0(0). source

Sentiment Analysis⚓︎

  • Rocklage, M. D., He, S., Rucker, D. D., & Nordgren, L. F. (2023). Beyond Sentiment: The Value and Measurement of Consumer Certainty in Language. Journal of Marketing Research, 0(0). https://doi.org/10.1177/00222437221134802

Decentralized Learning⚓︎

  • Li, Tian, et al. "Federated learning: Challenges, methods, and future directions." IEEE Signal Processing Magazine 37.3 (2020): 50-60. source
  • Lim, Wei Yang Bryan, et al. "Federated learning in mobile edge networks: A comprehensive survey." IEEE Communications Surveys & Tutorials 22.3 (2020): 2031-2063. source
  • Mothukuri, Viraaji, et al. "A survey on security and privacy of federated learning." Future Generation Computer Systems 115 (2021): 619-640. source
  • Kairouz, Peter, et al. "Advances and open problems in federated learning." Foundations and Trends® in Machine Learning 14.1–2 (2021): 1-210. source
  • Warnat-Herresthal, Stefanie, et al. "Swarm learning for decentralized and confidential clinical machine learning." Nature 594.7862 (2021): 265-270. source code
  • Kallista Bonawitz, et al. 2022. Federated learning and privacy. Commun. ACM 65, 4 (April 2022), 90–97. source

Personality Measurement⚓︎

  • Kai Yang, Raymond Y. K. Lau, Ahmed Abbasi (2022) Getting Personal: A Deep Learning Artifact for Text-Based Measurement of Personality. Information Systems Research 0(0). source

Adversaries⚓︎

  • Li, Weifeng, and Yidong Chai. "Assessing and Enhancing Adversarial Robustness of Predictive Analytics: An Empirically Tested Design Framework." Journal of Management Information Systems 39.2 (2022): 542-572. source

Data Imputation⚓︎

  • Lin, Wei-Chao, and Chih-Fong Tsai. "Missing value imputation: a review and analysis of the literature (2006–2017)." Artificial Intelligence Review 53.2 (2020): 1487-1509. source
  • Hasan, Md Kamrul, et al. "Missing value imputation affects the performance of machine learning: A review and analysis of the literature (2010–2021)." Informatics in Medicine Unlocked 27 (2021): 100799. source

Application⚓︎

  • Aiken, Emily, et al. "Machine learning and phone data can improve targeting of humanitarian aid." Nature (2022): 1-7. source
  • Nan Zhang, Heng Xu (2023) Fairness of Ratemaking for Catastrophe Insurance: Lessons from Machine Learning. Information Systems Research 0(0). https://doi.org/10.1287/isre.2022.1195
  • Arindam Ray, Wolfgang Jank, Kaushik Dutta, Matthew Mullarkey (2023) An LSTM+ Model for Managing Epidemics: Using Population Mobility and Vulnerability for Forecasting COVID-19 Hospital Admissions. INFORMS Journal on Computing 0(0). https://doi.org/10.1287/ijoc.2023.1269

Conversational Agents⚓︎

  • Elshan, E., Ebel, P., Söllner, M., & Leimeister, J. M. (2023). Leveraging Low Code Development of Smart Personal Assistants: An Integrated Design Approach with the SPADE Method. Journal of Management Information Systems, 40(1), 96-129. https://doi.org/10.1080/07421222.2023.2172776

Transparency⚓︎

  • Bitzer, T., Wiener, M., & Cram, W. (2023). Algorithmic Transparency: Concepts, Antecedents, and Consequences – A Review and Research Framework. Communications of the Association for Information Systems, 52, pp-pp. https://aisel.aisnet.org/cais/vol52/iss1/16

Graph And Network⚓︎

Graph Neural Network⚓︎

  • Kipf, T. N. "Deep learning with graph-structured representations." (2020). pdf
  • Wu, Zonghan, et al. "A comprehensive survey on graph neural networks." IEEE Transactions on Neural Networks and Learning Systems (2020). source
  • Zhou, Jie, et al. "Graph neural networks: A review of methods and applications." arXiv preprint arXiv:1812.08434 (2018). source
  • Zhang, Chuxu, et al. "Heterogeneous graph neural network." Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019. source
  • Wang, Xiao, et al. "Heterogeneous graph attention network." The World Wide Web Conference. 2019. source
  • Hu, Ziniu, et al. "Heterogeneous graph transformer." Proceedings of The Web Conference 2020. 2020. source

Graph Embedding⚓︎

  • Goyal, Palash, and Emilio Ferrara. "Graph embedding techniques, applications, and performance: A survey." Knowledge-Based Systems 151 (2018): 78-94. source
  • Xi Chen, Yan Liu, Cheng Zhang (2022) Distinguishing Homophily from Peer Influence Through Network Representation Learning. INFORMS Journal on Computing 0(0). source

Graphical Causality⚓︎

  • Bernhard Schölkopf, et al. "Towards Causal Representation Learning." (2021). source

Influence Maximization⚓︎

  • Li, Yuchen, et al. "Influence maximization on social graphs: A survey." IEEE Transactions on Knowledge and Data Engineering 30.10 (2018): 1852-1872. source
  • Banerjee, Suman, Mamata Jenamani, and Dilip Kumar Pratihar. "A survey on influence maximization in a social network." Knowledge and Information Systems 62.9 (2020): 3417-3455. source
  • De Nittis, Giuseppe, and Nicola Gatti. "How to maximize the spread of social influence: A survey." arXiv preprint arXiv:1806.07757 (2018). source
  • Ozan Candogan (2022) Persuasion in Networks: Public Signals and Cores. Operations Research 0(0). source

Vertical Markets⚓︎

  • Soheil Ghili (2022) Network Formation and Bargaining in Vertical Markets: The Case of Narrow Networks in Health Insurance. Marketing Science 0(0). source

Network Structures⚓︎

  • Sinan Aral, Paramveer S. Dhillon (2022) What (Exactly) Is Novelty in Networks? Unpacking the Vision Advantages of Brokers, Bridges, and Weak Ties. Management Science 0(0). source
  • Schecter, Aaron, Omid Nohadani, and Noshir Contractor. "A Robust Inference Method for Decision Making in Networks." Management Information Systems Quarterly 46.2 (2022): 713-738. source
  • Syngjoo Choi, Sanjeev Goyal, Frederic Moisan, Yu Yang Tony To (2023) Learning in Networks: An Experiment on Large Networks with Real-World Features. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4680

Network Privacy⚓︎

  • Marcella Hastings, Brett Hemenway Falk, Gerry Tsoukalas (2022) Privacy-Preserving Network Analytics. Management Science 0(0). source

Recommendation Systems⚓︎

Recommendation Objectives⚓︎

  • Dias, M. B., Locher, D., Li, M., El-Deredy, W., & Lisboa, P. J. (2008, October). The value of personalised recommender systems to e-business: a case study. In Proceedings of the 2008 ACM conference on Recommender systems (pp. 291-294). source
  • Gunawardana, Asela, and Guy Shani. "A survey of accuracy evaluation metrics of recommendation tasks." Journal of Machine Learning Research 10.12 (2009). source
  • Kaminskas, Marius, and Derek Bridge. "Diversity, serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems." ACM Transactions on Interactive Intelligent Systems (TiiS) 7.1 (2016): 1-42. source
  • Kunaver, Matevž, and Tomaž Požrl. "Diversity in recommender systems–A survey." Knowledge-based systems 123 (2017): 154-162. source
  • Wu, Qiong, et al. "Recent advances in diversified recommendation." arXiv preprint arXiv:1905.06589 (2019). source
  • Jannach, Dietmar, and Michael Jugovac. "Measuring the business value of recommender systems." ACM Transactions on Management Information Systems (TMIS) 10.4 (2019): 1-23. source
  • Wu, Le, et al. "A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation." IEEE Transactions on Knowledge and Data Engineering (2022). source
  • Alhijawi, Bushra, Arafat Awajan, and Salam Fraihat. "Survey on the Objectives of Recommender System: Measures, Solutions, Evaluation Methodology, and New Perspectives." ACM Computing Surveys (CSUR) (2022). source

Dataset⚓︎

  • Gao, Chongming, et al. "KuaiRec: A Fully-observed Dataset for Recommender Systems." arXiv preprint arXiv:2202.10842 (2022). source web
  • Chin, Jin Yao, Yile Chen, and Gao Cong. "The Datasets Dilemma: How Much Do We Really Know About Recommendation Datasets?." Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. 2022. source

Recommendation Simulator⚓︎

  • Lü, Linyuan, and Tao Zhou. "Link prediction in complex networks: A survey." Physica A: statistical mechanics and its applications 390.6 (2011): 1150-1170. source
  • Lü, Linyuan, and Tao Zhou. "Link prediction in complex networks: A survey." Physica A: statistical mechanics and its applications 390.6 (2011): 1150-1170. https://doi.org/10.1145/3012704
  • Wang, P., Xu, B., Wu, Y. et al. Link prediction in social networks: the state-of-the-art. Sci. China Inf. Sci. 58, 1–38 (2015). https://doi.org/10.1007/s11432-014-5237-y
  • Kim, J., Diesner, J. Formational bounds of link prediction in collaboration networks. Scientometrics 119, 687–706 (2019). https://doi.org/10.1007/s11192-019-03055-6
  • Kumar, Ajay, et al. "Link prediction techniques, applications, and performance: A survey." Physica A: Statistical Mechanics and its Applications 553 (2020): 124289. source
  • Qin, Meng, and Dit-Yan Yeung. "Temporal Link Prediction: A Unified Framework, Taxonomy, and Review." arXiv preprint arXiv:2210.08765 (2022). https://doi.org/10.48550/arXiv.2210.08765
  • Wu, H., Song, C., Ge, Y. et al. Link Prediction on Complex Networks: An Experimental Survey. Data Sci. Eng. 7, 253–278 (2022). https://doi.org/10.1007/s41019-022-00188-2

Recommendation Framework⚓︎

  • Anelli, Vito Walter, et al. "Elliot: a comprehensive and rigorous framework for reproducible recommender systems evaluation." Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2021. source code
  • TorchRec is a PyTorch domain library built to provide common sparsity & parallelism primitives needed for large-scale recommender systems (RecSys). It allows authors to train models with large embedding tables sharded across many GPUs.
  • Spotlight uses PyTorch to build both deep and shallow recommender models. By providing both a slew of building blocks for loss functions (various pointwise and pairwise ranking losses), representations (shallow factorization representations, deep sequence models), and utilities for fetching (or generating) recommendation datasets, it aims to be a tool for rapid exploration and prototyping of new recommender models.
  • MTReclib provides a PyTorch implementation of multi-task recommendation models and common datasets.
  • The repository microsoft/recommenders contains examples and best practices for building recommendation systems, provided as Jupyter notebooks.
  • Surprise is a Python scikit for building and analyzing recommender systems that deal with explicit rating data.
  • The repository hiroyuki-kasai/NMFLibrary is a pure-Matlab library of a collection of algorithms of non-negative matrix factorization (NMF).
  • QMF is a fast and scalable C++ library for implicit-feedback matrix factorization models (WALS and BPR).
  • The repository benfred/implicit provides fast Python implementations of several different popular recommendation algorithms for implicit feedback datasets.
  • This repository liu-yihong/BPRH implements the Bayesian personalized ranking method for heterogeneous implicit feedback.
  • reXmeX is recommender system evaluation metric library. It consists of utilities for recommender system evaluation. First, it provides a comprehensive collection of metrics for the evaluation of recommender systems. Second, it includes a variety of methods for reporting and plotting the performance results. Implemented metrics cover a range of well-known metrics and newly proposed metrics from data mining conferences and prominent journals.

Sequential Recommendation Systems⚓︎

  • Quadrana, Massimo, Paolo Cremonesi, and Dietmar Jannach. "Sequence-aware recommender systems." ACM Computing Surveys (CSUR) 51.4 (2018): 1-36. source
  • Maher, Mohamed, et al. "Comprehensive Empirical Evaluation of Deep Learning Approaches for Session-based Recommendation in E-Commerce." arXiv preprint arXiv:2010.12540 (2020). source
  • Fang, Hui, et al. "Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations." ACM Transactions on Information Systems (TOIS) 39.1 (2020): 1-42. source
  • Latifi, Sara, Noemi Mauro, and Dietmar Jannach. "Session-aware recommendation: A surprising quest for the state-of-the-art." Information Sciences 573 (2021): 291-315. source
  • Wang, Shoujin, et al. "A survey on session-based recommender systems." ACM Computing Surveys (CSUR) 54.7 (2021): 1-38. source
  • Wen Wang, Beibei Li, Xueming Luo, Xiaoyi Wang (2022) Deep Reinforcement Learning for Sequential Targeting. Management Science 0(0). source
  • Omid Rafieian (2022) Optimizing User Engagement Through Adaptive Ad Sequencing. Marketing Science 0(0). source
  • Yifu Li, Christopher Thomas Ryan, Lifei Sheng (2023) Optimal Sequencing in Single-Player Games. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4665
  • Marios Kokkodis, Panagiotis G. Ipeirotis (2023) The Good, the Bad, and the Unhirable: Recommending Job Applicants in Online Labor Markets. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4690

User-Item Matrix Factorization⚓︎

  • Su, Xiaoyuan, and Taghi M. Khoshgoftaar. "A survey of collaborative filtering techniques." Advances in artificial intelligence 2009 (2009). source
  • Cacheda, Fidel, et al. "Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems." ACM Transactions on the Web (TWEB) 5.1 (2011): 1-33. source
  • Shi, Yue, Martha Larson, and Alan Hanjalic. "Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges." ACM Computing Surveys (CSUR) 47.1 (2014): 1-45. source
  • Han, Soyeon Caren, et al. "GLocal-K: Global and Local Kernels for Recommender Systems." Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2021. source
  • Rendle, Steffen, et al. "Neural collaborative filtering vs. matrix factorization revisited." Fourteenth ACM conference on recommender systems. 2020. source

Graph Neural Network Based Recommendation⚓︎

  • Wu, Shiwen, et al. "Graph neural networks in recommender systems: a survey." arXiv preprint arXiv:2011.02260 (2020). source

Reinforcement Learning Based Recommendation⚓︎

  • Lin, Yuanguo, et al. "A Survey on Reinforcement Learning for Recommender Systems." arXiv preprint arXiv:2109.10665 (2021). source

Causal Learning⚓︎

  • Si, Zihua et al. “A Model-Agnostic Causal Learning Framework for Recommendation using Search Data.” (2022). source code

Self-Supervised Learning⚓︎

  • Yu, Junliang, et al. "Self-Supervised Learning for Recommender Systems: A Survey." arXiv preprint arXiv:2203.15876 (2022). source

Debias⚓︎

  • Schnabel, Tobias, et al. "Recommendations as treatments: Debiasing learning and evaluation." international conference on machine learning. PMLR, 2016. source
  • Chen, Jiawei, et al. "AutoDebias: Learning to Debias for Recommendation." arXiv preprint arXiv:2105.04170 (2021). source
  • Jiawei Chen on github.com provides a repository at jiawei-chen/RecDebiasing

User Reviews for Recommendation⚓︎

  • Sachdeva, Noveen, and Julian McAuley. "How useful are reviews for recommendation? a critical review and potential improvements." Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020. source

Regulations⚓︎

  • Tommaso Di Noia, et al. 2022. Recommender systems under European AI regulations. Commun. ACM 65, 4 (April 2022), 69–73. source

Healthcare⚓︎

  • Ali Hajjar, Oguzhan Alagoz (2022) Personalized Disease Screening Decisions Considering a Chronic Condition. Management Science 0(0). source
  • Xiang Hui, Zekun Liu, Weiqing Zhang (2023) From High Bar to Uneven Bars: The Impact of Information Granularity in Quality Certification. Management Science 0(0). https://pubsonline.informs.org/doi/abs/10.1287/isre.2022.1191
  • Josh C. D’Aeth, Shubhechyya Ghosal, Fiona Grimm, David Haw, Esma Koca, Krystal Lau, Huikang Liu, Stefano Moret, Dheeya Rizmie, Peter C. Smith, Giovanni Forchini, Marisa Miraldo, Wolfram Wiesemann (2023) Optimal Hospital Care Scheduling During the SARS-CoV-2 Pandemic. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4679
  • Johnson, M., Murthy, D., Robertson, B. W., Smith, W. R., & Stephens, K. K. (2023). Moving Emergency Response Forward: Leveraging Machine-Learning Classification of Disaster-Related Images Posted on Social Media. Journal of Management Information Systems, 40(1), 163-182. https://doi.org/10.1080/07421222.2023.2172778

Point-of-Interest⚓︎

  • Xiao-Jun Wang, Tao Liu, Weiguo Fan (2023) TGVx: Dynamic Personalized POI Deep Recommendation Model. INFORMS Journal on Computing 0(0). https://doi.org/10.1287/ijoc.2023.1286

Explainable Recommendation⚓︎

  • Zhang, Yongfeng, and Xu Chen. "Explainable recommendation: A survey and new perspectives." Foundations and Trends in Information Retrieval 14.1 (2020): 1-101. source
  • Chen, Xu, Yongfeng Zhang, and Ji-Rong Wen. "Measuring" Why" in Recommender Systems: a Comprehensive Survey on the Evaluation of Explainable Recommendation." arXiv preprint arXiv:2202.06466 (2022). source

Attacking Recommendation Systems⚓︎

  • Su, Xue-Feng, Hua-Jun Zeng, and Zheng Chen. "Finding group shilling in recommendation system." Special interest tracks and posters of the 14th international conference on World Wide Web. 2005. source
  • O'Donovan, John, and Barry Smyth. "Is trust robust? An analysis of trust-based recommendation." Proceedings of the 11th international conference on Intelligent user interfaces. 2006. source
  • Hurley, Neil J., Michael P. O'Mahony, and Guenole CM Silvestre. "Attacking recommender systems: A cost-benefit analysis." IEEE Intelligent Systems 22.3 (2007): 64-68. source
  • Patel, Krupa, et al. "A state of art survey on shilling attack in collaborative filtering based recommendation system." Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 1. Springer, Cham, 2016. source
  • Fang, Minghong, et al. "Poisoning attacks to graph-based recommender systems." Proceedings of the 34th Annual Computer Security Applications Conference. 2018. source
  • Hu, Rui, et al. "Targeted poisoning attacks on social recommender systems." 2019 IEEE Global Communications Conference (GLOBECOM). IEEE, 2019. source
  • Zhang, Hengtong, et al. "Practical data poisoning attack against next-item recommendation." Proceedings of The Web Conference 2020. 2020. source
  • Song, Junshuai, et al. "Poisonrec: an adaptive data poisoning framework for attacking black-box recommender systems." 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 2020. source
  • Wu, Zih-Wun, Chiao-Ting Chen, and Szu-Hao Huang. "Poisoning attacks against knowledge graph-based recommendation systems using deep reinforcement learning." Neural Computing and Applications (2021): 1-19. source
  • Chen, Liang, et al. "Data poisoning attacks on neighborhood‐based recommender systems." Transactions on Emerging Telecommunications Technologies 32.6 (2021): e3872. source
  • Zhang, Hengtong, et al. "Data Poisoning Attack against Recommender System Using Incomplete and Perturbed Data." Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2021. source
  • Fan, Wenqi, et al. "Attacking Black-box Recommendations via Copying Cross-domain User Profiles." 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2021. source

Diversity⚓︎

  • Kexin Yin, Xiao Fang, Bintong Chen, Olivia R. Liu Sheng (2022) Diversity Preference-Aware Link Recommendation for Online Social Networks. Information Systems Research 0(0). source

Multi-Sided⚓︎

  • Rastegari, Baharak, et al. "Two-sided matching with partial information." Proceedings of the fourteenth ACM conference on Electronic Commerce. 2013. https://doi.org/10.1145/2482540.2482607
  • Malgonde, Onkar, et al. "TAMING COMPLEXITY IN SEARCH MATCHING: TWO-SIDED RECOMMENDER SYSTEMS ON DIGITAL PLATFORMS." Mis Quarterly 44.1 (2020). https://aisel.aisnet.org/misq/vol44/iss1/5/
  • Malgonde, Onkar S., et al. "Managing Digital Platforms with Robust Multi-Sided Recommender Systems." Journal of Management Information Systems 39.4 (2022): 938-968. https://doi.org/10.1080/07421222.2022.2127440

Followee Recommendation⚓︎

  • Yaxuan Ran, Jiani Liu, Yishi Zhang (2023) Integrating Users’ Contextual Engagements with Their General Preferences: An Interpretable Followee Recommendation Method. INFORMS Journal on Computing 0(0). https://doi.org/10.1287/ijoc.2023.1284

Reference Learning⚓︎

  • Jiapeng Liu, Miłosz Kadziński, Xiuwu Liao (2023) Modeling Contingent Decision Behavior: A Bayesian Nonparametric Preference-Learning Approach. INFORMS Journal on Computing 0(0). https://doi.org/10.1287/ijoc.2023.1292

Implicit Feedback⚓︎

  • Jannach, D., Lerche, L., & Zanker, M. (2018). Recommending based on implicit feedback. In Social Information Access: Systems and Technologies (pp. 510-569). Cham: Springer International Publishing. https://link.springer.com/chapter/10.1007/978-3-319-90092-6_14
  • Wei, W., Ren, X., Tang, J., Wang, Q., Su, L., Cheng, S., ... & Huang, C. (2023). LLMRec: Large Language Models with Graph Augmentation for Recommendation. arXiv preprint arXiv:2311.00423 Source Code.

Large Language Model & Recommendations⚓︎

  • Li, L., Zhang, Y., Liu, D., & Chen, L. (2023). Large Language Models for Generative Recommendation: A Survey and Visionary Discussions. arXiv preprint arXiv:2309.01157.
  • Yang, Z., Wu, J., Luo, Y., Zhang, J., Yuan, Y., Zhang, A., ... & He, X. (2023). Large Language Model Can Interpret Latent Space of Sequential Recommender. arXiv preprint arXiv:2310.20487 Source Code.

Fairness in Recommender Systems⚓︎

  • Ekstrand, M. D., Das, A., Burke, R., & Diaz, F. (2012). Fairness in recommender systems. In Recommender systems handbook (pp. 679-707). New York, NY: Springer US. https://doi.org/10.1007/978-1-0716-2197-4_18
  • Wang, Y., Ma, W., Zhang, M., Liu, Y., & Ma, S. (2023). A survey on the fairness of recommender systems. ACM Transactions on Information Systems, 41(3), 1-43. https://doi.org/10.1145/3547333
  • Deldjoo, Y., Jannach, D., Bellogin, A., Difonzo, A., & Zanzonelli, D. (2024). Fairness in recommender systems: research landscape and future directions. User Modeling and User-Adapted Interaction, 34(1), 59-108. https://doi.org/10.1007/s11257-023-09364-z

Operations Research⚓︎

Polynomial Optimization⚓︎

  • Parrilo, P. A., & Sturmfels, B. (2001). Minimizing polynomial functions. arXiv preprint math/0103170.
  • Laurent, M. (2009). Sums of squares, moment matrices and optimization over polynomials. Emerging applications of algebraic geometry, 157-270. link

Lagrangian Relaxation⚓︎

  • Fisher, Marshall L. "The Lagrangian relaxation method for solving integer programming problems." Management science 27.1 (1981): 1-18. source
  • Ghoshal, Abhijeet, et al. "Hiding Sensitive Information when Sharing Distributed Transactional Data." Information systems research 31.2 (2020): 473-490. source

Column Generation⚓︎

  • Menon, Syam, and Sumit Sarkar. "Privacy and Big Data: Scalable Approaches to Sanitize Large Transactional Databases for Sharing." MIS Quarterly 40.4 (2016). source
  • Dash, Sanjeeb, Oktay Günlük, and Dennis Wei. "Boolean decision rules via column generation." arXiv preprint arXiv:1805.09901 (2018). source

Decision Under Uncertainty⚓︎

  • Sen, Suvrajeet, and Julia L. Higle. "An introductory tutorial on stochastic linear programming models." Interfaces 29.2 (1999): 33-61. source
  • Alessio Trivella, Danial Mohseni-Taheri, Selvaprabu Nadarajah (2022) Meeting Corporate Renewable Power Targets. Management Science 0(0). source

Data-Driven Optimization⚓︎

  • Gah-Yi Ban, Cynthia Rudin (2018) The Big Data Newsvendor: Practical Insights from Machine Learning. Operations Research 67(1):90-108. source
  • L. Jeff Hong, Zhiyuan Huang, Henry Lam (2020) Learning-Based Robust Optimization: Procedures and Statistical Guarantees. Management Science 67(6):3447-3467. source
  • Dimitris Bertsimas, Nihal Koduri (2021) Data-Driven Optimization: A Reproducing Kernel Hilbert Space Approach. Operations Research 70(1):454-471. source
  • Keliang Wang, Leonardo Lozano, Carlos Cardonha, David Bergman (2023) Optimizing over an Ensemble of Trained Neural Networks. INFORMS Journal on Computing 0(0). https://doi.org/10.1287/ijoc.2023.1285
  • Omar Besbes, Omar Mouchtaki (2023) How Big Should Your Data Really Be? Data-Driven Newsvendor: Learning One Sample at a Time. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4725

Predict-then-Optimize Paradigm⚓︎

  • Bertsimas, D., & Kallus, N. (2018). From Predictive to Prescriptive Analytics. ArXiv:1402.5481. source
  • Demirovic, E., Stuckey, P. J., Bailey, J., Chan, J., Leckie, C., Ramamohanarao, K., & Guns, T. (2019). Predict+Optimise with Ranking Objectives: Exhaustively Learning Linear Functions. 1078–1085.
  • Elmachtoub, A. N., & Grigas, P. (2020). Smart “Predict, then Optimize.” ArXiv:1710.08005. source
  • Mandi, J., Bucarey, V., Mulamba, M., & Guns, T. (2022). Predict and Optimize: Through the Lens of Learning to Rank. ArXiv:2112.03609. source

Multi-Objective Optimization⚓︎

  • Arne Herzel, Stefan Ruzika, Clemens Thielen (2021) Approximation Methods for Multiobjective Optimization Problems: A Survey. INFORMS Journal on Computing 33(4):1284-1299. source

E-Commerce⚓︎

  • Maximilian Schiffer, Nils Boysen, Patrick S. Klein, Gilbert Laporte, Marco Pavone (2022) Optimal Picking Policies in E-Commerce Warehouses. Management Science 0(0). source
  • Hanwei Li, David Simchi-Levi, Michelle Xiao Wu, Weiming Zhu (2022) Estimating and Exploiting the Impact of Photo Layout: A Structural Approach. Management Science 0(0). source
  • Goldstein, Anat; Oestreicher-Singer, Gal; Barzilay, Ohad; and Yahav, Inbal. 2022. "Are We There Yet? Analyzing Progress in the Conversion Funnel Using the Diversity of Searched Products," MIS Quarterly, (46: 4) pp.2015-2054. source

Assortment Optimization⚓︎

  • Zhen-Yu Chen, Zhi-Ping Fan, Minghe Sun (2022) Machine Learning Methods for Data-Driven Demand Estimation and Assortment Planning Considering Cross-Selling and Substitutions. INFORMS Journal on Computing 0(0). source
  • Santiago R. Balseiro, Antoine Désir (2022) Incentive-Compatible Assortment Optimization for Sponsored Products. Management Science 0(0). source
  • Antoine Désir, Vineet Goyal, Bo Jiang, Tian Xie, Jiawei Zhang (2023) Robust Assortment Optimization Under the Markov Chain Choice Model. Operations Research 0(0). https://doi.org/10.1287/opre.2022.2420
  • Ningyuan Chen, Andre A. Cire, Ming Hu, Saman Lagzi (2023) Model-Free Assortment Pricing with Transaction Data. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4651

Decision Analysis⚓︎

  • Eric Neyman, Tim Roughgarden (2023) From Proper Scoring Rules to Max-Min Optimal Forecast Aggregation. Operations Research 0(0). source
  • Ibrahim Abada, Xavier Lambin (2023) Artificial Intelligence: Can Seemingly Collusive Outcomes Be Avoided?. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4623
  • Asa B. Palley, Ville A. Satopää (2023) Boosting the Wisdom of Crowds Within a Single Judgment Problem: Weighted Averaging Based on Peer Predictions. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4648

Electric Vehicle⚓︎

  • Wei Qi, Yuli Zhang, Ningwei Zhang (2023) Scaling Up Electric-Vehicle Battery Swapping Services in Cities: A Joint Location and Repairable-Inventory Model. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4731

Probabilistic Reasoning⚓︎

  • Li, Zhepeng, et al. "Utility-based link recommendation for online social networks." Management Science 63.6 (2017): 1938-1952. source
  • Ghoshal, Abhijeet, Syam Menon, and Sumit Sarkar. "Recommendations using information from multiple association rules: A probabilistic approach." Information Systems Research 26.3 (2015): 532-551. source

Agent-based Modeling & Simulation⚓︎

  • Bonabeau, Eric. "Agent-based modeling: Methods and techniques for simulating human systems." Proceedings of the national academy of sciences 99.suppl 3 (2002): 7280-7287. source
  • Macal, Charles M., and Michael J. North. "Tutorial on agent-based modeling and simulation." Proceedings of the Winter Simulation Conference, 2005.. IEEE, 2005. source
  • Railsback, Steven F., Steven L. Lytinen, and Stephen K. Jackson. "Agent-based simulation platforms: Review and development recommendations." Simulation 82.9 (2006): 609-623. source
  • An, Li. "Modeling human decisions in coupled human and natural systems: Review of agent-based models." Ecological Modelling 229 (2012): 25-36. source
  • Abar, Sameera, et al. "Agent Based Modelling and Simulation tools: A review of the state-of-art software." Computer Science Review 24 (2017): 13-33. source
  • Mladenov, Martin, et al. "RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems." arXiv preprint arXiv:2103.08057 (2021). source official webiste github
  • Dong, John Qi (2022) "Using Simulation in Information Systems Research," Journal of the Association for Information Systems, 23(2), 408-417. source
  • Zhaolin Hu, L. Jeff Hong (2022) Robust Simulation with Likelihood-Ratio Constrained Input Uncertainty. INFORMS Journal on Computing 0(0). source
  • Lucy E. Morgan, Luke Rhodes-Leader, Russell R. Barton (2022) Reducing and Calibrating for Input Model Bias in Computer Simulation. INFORMS Journal on Computing 0(0). source

Video Content Structuring⚓︎

Team⚓︎

  • Devine, Dennis J., and Jennifer L. Philips. "Do smarter teams do better: A meta-analysis of cognitive ability and team performance." Small group research 32.5 (2001): 507-532. source
  • Kozlowski, Steve WJ, and Daniel R. Ilgen. "Enhancing the effectiveness of work groups and teams." Psychological science in the public interest 7.3 (2006): 77-124. source
  • Wang, Xinyu, Zhou Zhao, and Wilfred Ng. "A comparative study of team formation in social networks." International conference on database systems for advanced applications. Springer, Cham, 2015. source
  • Andrejczuk, Ewa, et al. "The composition and formation of effective teams: computer science meets organizational psychology." The Knowledge Engineering Review 33 (2018). source
  • Gómez-Zará, Diego, Leslie A. DeChurch, and Noshir S. Contractor. "A taxonomy of team-assembly systems: Understanding how people use technologies to form teams." Proceedings of the ACM on Human-Computer Interaction 4.CSCW2 (2020): 1-36. source
  • Juárez, Julio, Cipriano Santos, and Carlos A. Brizuela. "A Comprehensive Review and a Taxonomy Proposal of Team Formation Problems." ACM Computing Surveys (CSUR) 54.7 (2021): 1-33. source

User Behavior⚓︎

Mobile⚓︎

  • Shaohui Wu, Yong Tan, Yubo Chen, Yitian (Sky) Liang (2022) How Is Mobile User Behavior Different?—A Hidden Markov Model of Cross-Mobile Application Usage Dynamics. Information Systems Research 0(0) source
  • Raluca M. Ursu, Qianyun Zhang, Elisabeth Honka (2022) Search Gaps and Consumer Fatigue. Marketing Science 0(0). source

Behavior Change⚓︎

  • Merz, M., & Steinherr, V. M. (2022). Process-based Guidance for Designing Behavior Change Support Systems: Marrying the Persuasive Systems Design Model to the Transtheoretical Model of Behavior Change. Communications of the Association for Information Systems, 50, pp-pp. source

Pricing⚓︎

  • Jinzhi Bu, David Simchi-Levi, Li Wang (2022) Offline Pricing and Demand Learning with Censored Data. Management Science 0(0). source
  • Wen Chen, Ying He, Saurabh Bansal (2023) Customized Dynamic Pricing When Customers Develop a Habit or Satiation. Operations Research 0(0). https://pubsonline.informs.org/doi/abs/10.1287/opre.2022.2412

Dynamic Pricing⚓︎

  • N. Bora Keskin, Yuexing Li, Jing-Sheng Song (2022) Data-Driven Dynamic Pricing and Ordering with Perishable Inventory in a Changing Environment. Management Science 0(0). source
  • Jinzhi Bu, David Simchi-Levi, Yunzong Xu (2022) Online Pricing with Offline Data: Phase Transition and Inverse Square Law. Management Science 0(0). source

Auditing⚓︎

  • Bouayad, Lina, Balaji Padmanabhan, and Kaushal Chari. "Audit Policies Under the Sentinel Effect: Deterrence-Driven Algorithms." Information Systems Research 30.2 (2019): 466-485. source

Reliable Prediction⚓︎

  • Romano, Yaniv, Evan Patterson, and Emmanuel Candes. "Conformalized quantile regression." Advances in neural information processing systems 32 (2019). source code
  • Sesia, Matteo, and Emmanuel J. Candès. "A comparison of some conformal quantile regression methods." Stat 9.1 (2020): e261. source
  • Model Agnostic Prediction Interval Estimator (MAPIE) is a python toolkit for prediction interval estimation.
  • Nam Ho-Nguyen, Fatma Kılınç-Karzan (2022) Risk Guarantees for End-to-End Prediction and Optimization Processes. Management Science 0(0). source

Online Platforms⚓︎

  • Nicole Immorlica, Brendan Lucier, Vahideh Manshadi, Alexander Wei (2022) Designing Approximately Optimal Search on Matching Platforms. Management Science 0(0). source

Advertising⚓︎

  • Ranjit M. Christopher, Sungho Park, Sang Pil Han, Min-Kyu Kim (2022) Bypassing Performance Optimizers of Real Time Bidding Systems in Display Ad Valuation. Information Systems Research 0(0). source
  • Jessica Clark, Jean-François Paiement, Foster Provost (2023) Who’s Watching TV?. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1204

Artifact Generalization⚓︎

  • Manoj A. Thomas , Yan Li , Allen S. Lee (2022) Generalizing the Information Systems Artifact. Information Systems Research 0(0). source

Healthcare⚓︎

  • John R. Birge, Ozan Candogan, Yiding Feng (2022) Controlling Epidemic Spread: Reducing Economic Losses with Targeted Closures. Management Science 0(0). source
  • Yu, Shuo; Chai, Yidong; Chen, Hsinchun; Sherman, Scott J.; and Brown, Randall A.. 2022. "Wearable Sensor-Based Chronic Condition Severity Assessment: An Adversarial Attention-Based Deep Multisource Multitask Learning Approach," MIS Quarterly, (46: 3) pp.1355-1394. source
  • Wanyi Chen, Nilay Tanik Argon, Tommy Bohrmann, Benjamin Linthicum, Kenneth Lopiano, Abhishek Mehrotra, Debbie Travers, Serhan Ziya (2022) Using Hospital Admission Predictions at Triage for Improving Patient Length of Stay in Emergency Departments. Operations Research 0(0). source
  • Shuo Yu, Yidong Chai, Sagar Samtani, Hongyan Liu, Hsinchun Chen (2023) Motion Sensor–Based Fall Prevention for Senior Care: A Hidden Markov Model with Generative Adversarial Network Approach. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1203
  • Matt Baucum, Anahita Khojandi, Rama Vasudevan, Ritesh Ramdhani (2023) Optimizing Patient-Specific Medication Regimen Policies Using Wearable Sensors in Parkinson’s Disease. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4747

Security⚓︎

  • Warut Khern-am-nuai, Matthew J. Hashim, Alain Pinsonneault, Weining Yang, Ninghui Li (2022) Augmenting Password Strength Meter Design Using the Elaboration Likelihood Model: Evidence from Randomized Experiments. Information Systems Research 0(0). source

Bot Detection⚓︎

  • Victor Benjamin, T. S. Raghu (2022) Augmenting Social Bot Detection with Crowd-Generated Labels. Information Systems Research 0(0). source

Inventory Management⚓︎

  • Meng Qi, Yuanyuan Shi, Yongzhi Qi, Chenxin Ma, Rong Yuan, Di Wu, Zuo-Jun (Max) Shen (2022) A Practical End-to-End Inventory Management Model with Deep Learning. Management Science 0(0). source

Auction⚓︎

  • Benedikt Bünz, Benjamin Lubin, Sven Seuken (2022) Designing Core-Selecting Payment Rules: A Computational Search Approach. Information Systems Research 33(4):1157-1173. source

Fraud Detection⚓︎

  • Weinmann, Markus; Valacich, Joseph; Schneider, Christoph; Jenkins, Jeffrey L.; and Hibbeln, Martin. 2022. "The Path of the Righteous: Using Trace Data to Understand Fraud Decisions in Real Time (Open Access)," MIS Quarterly, (46: 4) pp.2317-2336. source

Retail⚓︎

  • Junyu Cao, Wei Qi (2022) Stall Economy: The Value of Mobility in Retail on Wheels. Operations Research 0(0). source

Matching⚓︎

  • Yiding Feng, Rad Niazadeh, Amin Saberi (2023) Two-Stage Stochastic Matching and Pricing with Applications to Ride Hailing. Operations Research 0(0). https://doi.org/10.1287/opre.2022.2398

Response Prediction⚓︎

  • Gang Chen, Shuaiyong Xiao, Chenghong Zhang, Huimin Zhao (2023) A Theory-Driven Deep Learning Method for Voice Chat–Based Customer Response Prediction. Information Systems Research 0(0). https://doi.org/10.1287/isre.2022.1196

Risk Prediction⚓︎

  • Yang, Yi, et al. "Unlocking the Power of Voice for Financial Risk Prediction: A Theory-Driven Deep Learning Design Approach." Management Information Systems Quarterly 47.1 (2023): 63-96. https://aisel.aisnet.org/misq/vol47/iss1/5

Online Reviews⚓︎

  • Yu, Yifan, et al. "Unifying Algorithmic and Theoretical Perspectives: Emotions in Online Reviews and Sales."Management Information Systems Quarterly 47.1 (2023): 127-160. https://aisel.aisnet.org/misq/vol47/iss1/7
  • Yang, Mingwen, et al. "Responding to Online Reviews in Competitive Markets: A Controlled Diffusion Approach." Management Information Systems Quarterly 47.1 (2023): 161-194. https://aisel.aisnet.org/misq/vol47/iss1/8

Product Design⚓︎

  • Alex Burnap, John R. Hauser, Artem Timoshenko (2023) Product Aesthetic Design: A Machine Learning Augmentation. Marketing Science 0(0). https://doi.org/10.1287/mksc.2022.1429

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Last Update On 2024-10-13.