Managing Digital Platforms with Robust Multi-Sided Recommender Systems

References

• Adomavicius, G.; and Kwon, Y. Improving aggregate recommendation diversity using ranking-based techniques. IEEE Transactions on Knowledge and Data Engineering, 24, 5 (2012), 896–911.
   Web of Science ®Google Scholar
• Adomavicius, G.; Manouselis, N., and Kwon, Y. Multi-criteria recommender systems. In . Ricci, Francesco, Rokach, Lior, Shapira, Bracha, Kantor, Paul B., Recommender Systems Handbook. Boston: Springer, 2011, pp. 769–803.
   Google Scholar
• Adomavicius, G.; and Tuzhilin, A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17, 6 (2005), 734–749.
   Web of Science ®Google Scholar
• Anderson, S.P.; and Coate, S. Market provision of broadcasting: A welfare analysis. The Review of Economic Studies, 72, 4 (2005), 947–972.
   Web of Science ®Google Scholar
• Asvanund, A.; Clay, K.; Krishnan, R.; & Smith, M. An empirical analysis of network externalities in peer-to-peer music-sharing networks. Information Systems Research, 15, 2 (2004), 155–174.
   Web of Science ®Google Scholar
• Bakos, Y.; and Katsamakas, E. Design and ownership of two-sided networks: Implications for internet platforms. Journal of Management Information Systems, 25, 2 (2008), 171–202.
   Web of Science ®Google Scholar
• Ben-Tal, A.; El Ghaoui, L.; and Nemirovski, A. Robust Optimization. Princeton: Princeton University Press, 2009.
   Google Scholar
• Ben-Tal, A.; and Nemirovski, A. Robust solutions of linear programming problems contaminated with uncertain data. Mathematical Programming, 88, 3 (2000), 411–424.
   Web of Science ®Google Scholar
• Bertsimas, D.; Brown, D.; and Caramanis, C. Theory and applications of robust optimization. SIAM Review, 53, 3 (2011), 464–501.
   Web of Science ®Google Scholar
• Bertsimas, D.; and Sim, M. The price of robustness. Operations Research, 52, 1 (2004), 35–53.
   Web of Science ®Google Scholar
• Bilali, A.; Engelhardt, R.; Dandl, F.; Fastenrath, U.; and Bogenberger, K. Analytical and agent-based model to evaluate ride-pooling impact factors. Transportation Research Record, 2674, 6 (2020), 1–12.
   Web of Science ®Google Scholar
• Boudreau, K., and Hagiu, A. Platform rules: Multi-sided platforms as regulators. In A. Gawer (ed), Platforms, Markets, and Innovation. Edward Elgar: Northampton, US, 2009, pp. 163–191.
   Google Scholar
• Brynjolfsson, E.; Hu, Y.; and Simester, D. Goodbye Pareto Principle, Hello Long Tail: The effect of search costs on the concentration of product sales. Management Science, 57, 8 (2011), 1373–1386.
   Web of Science ®Google Scholar
• Burke, R.; and H. Abdollahpouri. Educational recommendation with multiple stakeholders. In IEEE/WIC/ACM International Conference on Web Intelligence Workshops (WIW), Omaha, NE, 2016.
   Google Scholar
• Butler, B. Membership size, communication activity, and sustainability: A resource-based model of online social structures. Information Systems Research, 12, 4 (2001), 346–362.
   Web of Science ®Google Scholar
• Cambreleng, B. Fed-up French travellers face traffic chaos over festive period. In Yahoo News, 2019. Accessed 10 October 2022. https://www.france24.com/en/20191221-fed-up-french-travellers-face-traffic-chaos-over-festive-period
   Google Scholar
• Chen, P.-Y.; Chou, Y.-C., and Kauffman, R. Community-based Recommender Systems: Analyzing Business Models from a Systems Operator’s Perspective. 2009 42nd Hawaii International Conference on System Sciences, USA, 2009, IEEE. p. 1–10. doi:10.1109/HICSS.2009.117.
   Google Scholar
• Constantinides, P.; Henfridsson, O.; and Parker, G. Introduction-platforms and infrastructures in the digital age. Information Systems Research, 29, 2 (2018), 381–400.
   Web of Science ®Google Scholar
• Davis, J.; Eisenhardt, K.; and Bingham, C. Developing theory through simulation methods, Academy of Management Review, 32, 2 (2007), 480–499.
   Web of Science ®Google Scholar
• Dong, J.Q. Using simulation in information systems research. Journal of the Association for Information Systems, 23, 2 (2022), 408–417.
   Web of Science ®Google Scholar
• El-Ghaoui, L.; and Lebret, H. Robust solutions to least-square problems to uncertain data matrices. SIMA Journal on Matrix Analysis and Applications, 18(1997), 1035–1064.
   Google Scholar
• Evans, D. S; and The industrial organization of markets with two-sided platforms. Cambridge, MA: Competition Policy International, 2011. https://lccn.loc.gov/2005618920
   Google Scholar
• Fleder, D.; and Hosanagar, K. Blockbuster culture’s next rise or fall: The impact of recommender systems on sales diversity. Management Science, 55, 5 (2009), 697–712.
   Web of Science ®Google Scholar
• Gordon, L. Four coursera online classes are deemed worthy of college credit. In The Los Angeles Times, 2013.
   Google Scholar
• Hinz, O.; Otter, T.; Skiera, B. Estimating network effects in two-sided markets. Journal of Management Information Systems, 37, 1 (2020), 12–38.
   Web of Science ®Google Scholar
• Holland, J. The global economy as an adaptive system. In P. Anderson, K. Arrow, and D. Pines (eds.), The Economy as a Complex Evolving System. Reading, MA: Addison-Wesley, 1988.
   Google Scholar
• Jambor, T.; and J. Wang. Optimizing multiple objectives in collaborative filtering. In Proceedings of the Fourth ACM Conference on Recommender Systems, Barcelona, Spain, 2010, pp. 55–62.
   Google Scholar
• Jannach, D., and Adomavicius, G. Recommendations with a purpose. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16). Association for Computing Machinery, New York, NY, USA, 2016, pp. 7–10. doi: 10.1145/2959100.2959186.
   Google Scholar
• Jannach, D., and Adomavicius, G. Price and profit awareness in recommender systems. In Workshop on Value-Aware and Multi-Stakeholder Recommendation (VAMS), Como, Italy , 2017. https://arxiv.org/abs/1707.08029
   Google Scholar
• Jones, Q.; Ravid, G.; and Rafaeli, S. Information overload and the message dynamics of online interaction spaces: A theoretical model and empirical exploration. Information Systems Research, 15, 2 (2004), 194–210.
   Web of Science ®Google Scholar
• Kang, C.; Wang, X.; Chang, Y., and Tseng, B. Learning to rank with multi-aspect relevance for vertical search. In Proceedings of the fifth ACM International Conference on Web Search and Data Mining, New York, USA. p.453–462, 2012.
   Google Scholar
• Ketter, W.; Collins, J.; Saar-Tsechansky, M.; & Marom, O. Information systems for a smart electricity grid: Emerging challenges and opportunities. ACM Transactions on Management Information Systems (TMIS), 9, 3 (2018), 1–22.
   Web of Science ®Google Scholar
• Li, J.; Embry, P.; Mattingly, S.; Sadabadi, K.; Rasmidatta, I.; and Burris, M. Who chooses to carpool and why? Examination of Texas carpoolers. Transportation Research Record, 2021, 1 (2007), 110–117.
   Google Scholar
• Magnusson, K. Interpreting Cohen’s d Effect Size: An Interactive Visualization [Software]. Accessed 10 October 2022. https://rpsychologist.com/cohend/
   Google Scholar
• Malgonde, O.; Zhang, H.; Padmanabhan, B.; and Limayem, M. Taming complexity in search matching: Two-sided recommender systems on digital platforms. MIS Quarterly, 44, 1 (2020), 49–84.
   Web of Science ®Google Scholar
• Meyers, L. Uber Meets Taxis’ Immovable object: Market equilibrium. In The Observer. 2015. Accessed 10 October 2022. https://observer.com/2015/09/uber-meets-taxis-immovable-object-market-equilibrium/
   Google Scholar
• Munkres, J. Algorithms for the assignment and transportation problems. Journal of the Society for Industrial and Applied Mathematics, 5, 1 (1957), 32–38.
   Google Scholar
• Nan, N. Capturing bottom-up information technology use processes: A complex adaptive systems model. MIS Quarterly, 35, 2 (2011), 505–532.
   Web of Science ®Google Scholar
• Nguyen, P.; Dines, J.; Krasnodebski, J. A Multi-objective learning to re-rank approach to optimize online marketplaces for multiple stakeholders. In Workshop on Value-Aware and Multi-Stakeholder Recommendation (VAMS), Como, Italy, 2017.
   Google Scholar
• Qiu, L.; Tang, Q.; and Whinston, A. Two formulas for success in social media: Learning and network effects. Journal of Management Information Systems, 32, 4 (2015), 78–108.
   Web of Science ®Google Scholar
• Ricci, F.; Rokach, L.; and Shapira, B. Recommender Systems Handbook. Boston: Springer, 2011.
   Google Scholar
• Rodriguez, M.; Posse,C., and Zhang, E. Multiple objective optimization in recommender systems. In Proceedings of the Sixth ACM Conference on Recommender Systems, 11-18, Dublin Ireland, 2012. doi:10.1145/2365952.2365961
   Google Scholar
• Shani, G.; and Gunawardana, A. Evaluating Recommendation Systems. Boston: Springer, 2011.
   Google Scholar
• Shapiro, C.; and Varian, H. Information Rules: A Strategic Guide to the Network Economy. Boston: Harvard Business School Press, 1999.
   Google Scholar
• Soyster, A. Convex programming with set-inclusive constraints and applications to inexact linear programming. Operations Research, 21, 5 (1973), 1154–1157.
   Web of Science ®Google Scholar
• Sullivan, G.; and Feinn, R. Using Effect size—or Why the P value is not enough. Journal of Graduate Medical Education, 4, 3 (2012), 279–282.
   PubMedGoogle Scholar
• Sundt, A.; Luo, Q.; Vincent, J.; Shahabi, M.; and Yin, Y. Heuristics for customer-focused ride-pooling assignment. arXiv preprint arXiv:2107.11318, 2021.
   Google Scholar
• Sürer, Ö., R. Burke, and E.C. Malthouse. Multistakeholder recommendation with provider constraints. In Proceedings of the 12th ACM Conference on Recommender Systems, 54-62, Vancouver, Canada, 2018.
   Google Scholar
• Parker, G.; Van Alstyne, M.; and Choudary, S. Platform Revolution. New York: W. W. Norton & Company, 2016.
   Google Scholar
• Thomas, L.D.W.; Autio, E.; and Gann, D. Architectural leverage: Putting platforms in context. The Academy of Management Perspectives, 28, 2, (2014), 198–219.
   Web of Science ®Google Scholar
• Wang, L.; Bennett, P., and Collins-Thompson, K. Robust ranking models via risk-sensitive optimization. In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, Oregon, USA: Association for Computing Machinery, 2012, pp. 761–770 .
   Google Scholar
• Yoo, B.; Choudhary, V.; and Mukhopadhyay, T. A model of neutral B2B intermediaries. Journal of Management Information Systems, 19, 3 (2002), 43–68.
   Web of Science ®Google Scholar
• Zhang, J.; Adomavicius, G.; Gupta, A.; and Ketter, W. Consumption and performance: understanding longitudinal dynamics of recommender systems via an agent-based simulation framework. Information Systems Research, 31, 1 (2020), 76–101.
   Web of Science ®Google Scholar