Adaptive Data Acquisition for Personalized Recommendations with Optimality Guarantees on Short-Form Video Platforms
References
- (2016) Threshold bandits, with and without censored feedback. Adv. Neural Inform. Processing Systems, vol. 29 (Curran Associates Inc., Red Hook, NY), 4889–4897. Google Scholar
- (2023) Market segmentation trees. Manufacturing Service Oper. Management 25(2):648–667.Link, Google Scholar
- (2021) Dynamic pricing with finite price sets: A non-parametric approach. Math. Methods Oper. Res. 94(1):1–34.Crossref, Google Scholar
- (2018) Leveraging comparables for new product sales forecasting. Production Oper. Management 27(12):2340–2343.Crossref, Google Scholar
- (2019) Dynamic procurement of new products with covariate information: The residual tree method. Manufacturing Service Oper. Management 21(4):798–815.Link, Google Scholar
- (2022a) Applied machine learning in operations management. Babich V, Birge JR, Hilary G, eds. Innovative Technology at the Interface of Finance and Operations, Springer Series in Supply Chain Management, vol. 11 (Springer, Cham, Switzerland), 189–222.Crossref, Google Scholar
- (2022b) Learning personalized product recommendations with customer disengagement. Manufacturing Service Oper. Management 24(4):2010–2028.Link, Google Scholar
- (2019) A dynamic clustering approach to data-driven assortment personalization. Management Sci. 65(5):2095–2115.Abstract, Google Scholar
- (2016) Optimization in online content recommendation services: Beyond click-through rates. Manufacturing Service Oper. Management 18(1):15–33.Link, Google Scholar
- (2013) Multiple identifications in multi-armed bandits. Internat. Conf. Machine Learn. (PMLR, New York), 258–265.Google Scholar
- (2019) Dynamic learning of sequential choice bandit problem under marketing fatigue. Proc. AAAI Conf. Artificial Intelligence 33(1):3264–3271.Google Scholar
- (2019) Doubly adaptive cascading bandits with user abandonment. Preprint, submitted April 8, http://dx.doi.org/10.2139/ssrn.3355211.Google Scholar
- (2004) Lotus: An algorithm for building accurate and comprehensible logistic regression trees. J. Comput. Graph. Statist. 13(4):826–852.Crossref, Google Scholar
- (2021) Nonparametric pricing analytics with customer covariates. Oper. Res. 69(3):974–984.Link, Google Scholar
- (2015) On the optimal sample complexity for best arm identification. Preprint, submitted November 12, https://arxiv.org/abs/1511.03774.Google Scholar
- (2022) Decision forest: A nonparametric approach to modeling irrational choice. Management Sci. 68(10):7090–7111.Link, Google Scholar
- (2019) The use of binary choice forests to model and estimate discrete choices. Preprint, submitted August 3, https://arxiv.org/abs/1908.01109.Google Scholar
- (2017b) Nearly instance optimal sample complexity bounds for top-k arm selection. Proc. 20th Internat. Conf. Artificial Intelligence Statist. (PMLR, New York), 101–110.Google Scholar
- (2017a) Adaptive multiple-arm identification. Internat. Conf. Machine Learn. (PMLR, New York), 722–730.Google Scholar
- (2023) Model-free assortment pricing with transaction data. Management Sci. 69(10):5830–5847.Link, Google Scholar
- (2014) Combinatorial pure exploration of multi-armed bandits. Adv. Neural Inform. Processing Systems, vol. 27 (Curran Associates Inc., Red Hook, NY).Google Scholar
- (2020) Why short-form video apps are so popular in China. eMarketer (January 2), https://www.emarketer.com/content/why-short-form-video-apps-are-so-popular-in-china.Google Scholar
- (2017) Dynamic pricing and demand learning with limited price experimentation. Oper. Res. 65(6):1722–1731.Link, Google Scholar
- (2022) Data aggregation and demand prediction. Oper. Res. 70(5):2597–2618.Link, Google Scholar
- (2022) Product ranking on online platforms. Management Sci. 68(6):4024–4041.Link, Google Scholar
- (2014) Doubly robust policy evaluation and optimization. Statist. Sci. 29(4):485–511.Crossref, Google Scholar
- (2017) A practical method for solving contextual bandit problems using decision trees. Preprint, submitted June 14, https://arxiv.org/abs/1706.04687.Google Scholar
- (2006) Action elimination and stopping conditions for the multi-armed bandit and reinforcement learning problems. J. Machine Learn. Res. 7(39):1079–1105.Google Scholar
- (2016) Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing Service Oper. Management 18(1):69–88.Link, Google Scholar
- (2019) Sequential experimental design for transductive linear bandits. Adv. Neural Inform. Processing Systems, vol. 33 (Curran Associates Inc., Red Hook, NY).Google Scholar
- (2012) Best arm identification: A unified approach to fixed budget and fixed confidence. Adv. Neural Inform. Processing Systems, vol. 25 (Curran Associates Inc., Red Hook, NY).Google Scholar
- (2019) Off-policy deep reinforcement learning by bootstrapping the covariate shift. Proc. AAAI Conf. Artificial Intelligence 33(1):3647–3655.Google Scholar
- (2014) Online clustering of bandits. Internat. Conf. Machine Learn. (PMLR, New York), 757–765.Google Scholar
- (2017) On context-dependent clustering of bandits. Internat. Conf. Machine Learn. (PMLR, New York), 1253–1262.Google Scholar
- (2009) Covariate shift by kernel mean matching. Dataset Shift Machine Learn. (MIT Press, Cambridge, MA), 131–160.Google Scholar
- (2019) Forecasting new product life cycle curves: Practical approach and empirical analysis. Manufacturing Service Oper. Management 21(1):66–85.Link, Google Scholar
- (2007) Analyzing consumer-product graphs: Empirical findings and applications in recommender systems. Management Sci. 53(7):1146–1164.Link, Google Scholar
- HubSpot (2025) The top video marketing tactics brands are investing in [+which are losing steam]. Accessed August 10, 2025, https://blog.hubspot.com/marketing/top-video-marketing-tactics?Google Scholar
- (2018) A model-based embedding technique for segmenting customers. Oper. Res. 66(5):1247–1267.Link, Google Scholar
- (2020) Optimal best-arm identification in linear bandits. Adv. Neural Inform. Processing Systems, vol. 33 (Curran Associates Inc., Red Hook, NY), 10007–10017. Google Scholar
- (2017) Practical algorithms for best-K identification in multi-armed bandits. Preprint, submitted May 19, https://arxiv.org/abs/1705.06894.Google Scholar
- (2010) Efficient selection of multiple bandit arms: Theory and practice. Proc. 27th Internat. Conf. Internat. Conf. Machine Learn. (Omnipress, Madison, WI).Google Scholar
- (2013) Almost optimal exploration in multi-armed bandits. Internat. Conf. Machine Learn. (PMLR, New York), 1238–1246.Google Scholar
- (2016) On the complexity of best-arm identification in multi-armed bandit models. J. Machine Learn. Res. 17(1):1–42.Google Scholar
- (2021) Best arm identification in generalized linear bandits. Oper. Res. Lett. 49(3):365–371.Crossref, Google Scholar
- (2024) Data-driven clustering and feature-based retail electricity pricing with smart meters. Oper. Res., ePub ahead of print September 3, https://doi.org/10.1287/opre.2022.0112.Link, Google Scholar
- (2019) Measuring the value of recommendation links on product demand. Inform. Systems Res. 30(3):819–838.Link, Google Scholar
- (2005) Logistic model trees. Machine Learn. 59(1–2):161–205.Crossref, Google Scholar
- (2020) Bandit Algorithms (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
- (2024) Calibration of heterogeneous treatment effects in randomized experiments. Inform. Systems Res. 35(4):1721–1742.Link, Google Scholar
- (2020) Interpretable recommendations and user-centric explanations with geometric deep learning. Preprint, submitted November 13, https://dx.doi.org/10.2139/ssrn.3696092.Google Scholar
- (2021) Content-based model of web search behavior: An application to TV show search. Management Sci. 67(10):6378–6398.Link, Google Scholar
- (2020) This is why you heard about TikTok so much in 2020. New York Times (December 31), https://www.nytimes.com/2020/12/31/style/tiktok-trends-2020.html.Google Scholar
- (2020) Finding all ε-good arms in stochastic bandits. Adv. Neural Inform. Processing Systems, vol. 33 (Curran Associates Inc., Red Hook, NY).Google Scholar
- (2021) Racism, hate speech, and social media: A systematic review and critique. Television New Media 22(2):205–224.Crossref, Google Scholar
- (2022) Context-based dynamic pricing with online clustering. Production Oper. Management 31(9):3559–3575.Crossref, Google Scholar
- (2020) Optimization of tree ensembles. Oper. Res. 68(5):1605–1624.Link, Google Scholar
- (2014) Dynamic clustering of contextual multi-armed bandits. Proc. 23rd ACM Internat. Conf. Inform. Knowledge Management (ACM, New York), 1959–1962.Google Scholar
- (1992) Learning with continuous classes. Proc. 5th Australian Joint Conf. Artificial Intelligence, vol. 92 (World Scientific, Singapore), 343–348.Google Scholar
- (2019) Exploring k out of top ρ fraction of arms in stochastic bandits. 22nd Internat. Conf. Artificial Intelligence Statist. (PMLR, New York), 2820–2828.Google Scholar
- (1991) Minimum quality standards, fixed costs, and competition. RAND J. Econom. 22(4):490–504.Crossref, Google Scholar
- (2016) Simple Bayesian algorithms for best arm identification. Conf. Learn. Theory (PMLR, New York), 1417–1418.Google Scholar
- (2017) Customer acquisition via display advertising using multi-armed bandit experiments. Marketing Sci. 36(4):500–522.Link, Google Scholar
- (2014) Lectures on Stochastic Programming: Modeling and Theory (SIAM, Philadelphia).Crossref, Google Scholar
- (2020) An economic analysis of product recommendation in the presence of quality and taste-match heterogeneity. Inform. Systems Res. 31(2):399–411.Link, Google Scholar
- (2014) Best-arm identification in linear bandits. Adv. Neural Inform. Processing Systems, vol. 27 (Curran Associates Inc., Red Hook, NY).Google Scholar
- (2007) Mixture regression for covariate shift. Adv. Neural Inform. Processing Systems, vol. 19 (Curran Associates Inc., Red Hook, NY), 1337.Crossref, Google Scholar
- (2018) Best arm identification in linear bandits with linear dimension dependency. Internat. Conf. Machine Learn. (PMLR, New York), 4877–4886.Google Scholar
- Wallaroo (2021) TikTok statistics. (May 7), https://wallaroomedia.com/blog/social-media/tiktok-statistics/.Google Scholar
- (2018) A fully adaptive algorithm for pure exploration in linear bandits. Internat. Conf. Artificial Intelligence Statist. (PMLR, New York), 843–851.Google Scholar
- (2016) Buyer targeting optimization: A unified customer segmentation perspective. 2016 IEEE Internat. Conf. Big Data (IEEE, Piscataway, NJ), 1262–1271.Google Scholar
- (2006) Active learning via transductive experimental design. Proc. 23rd Internat. Conf. Machine Learn. (PMLR, New York), 1081–1088.Google Scholar
- (2022) NetEase cloud music data. Manufacturing Service Oper. Management 24(1):275–284.Link, Google Scholar

