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Greedy-Bayes for Targeted News Dissemination

Abstract : This work addresses user targeting for news content delivery. Specifically, we wish to disseminate a fresh news content, whose topic is yet unknown, to all interested users, while " spamming " a minimum number of uninterested users. We formulate this as an online stochastic optimization problem that extends in several ways the classical multi-armed bandit problem. We introduce Greedy-Bayes, a policy with appealing ro-bustness properties. We establish optimal scaling of a suitably defined regret measure in various scenarios of interest. To that end we develop an original proof technique based on martingale concentration inequalities. Numerical experiments show that Greedy-Bayes improves upon Thompson sampling, the state-of-the-art algorithm for bandit problems. Our analysis further implies that low regret can only be achieved if the assessment of content relevance for one user leverages feedback from users with widely distinct tastes. This impacts the design of efficient news dissemination platforms: existing systems typically do not leverage such negative feedback and could hence be improved upon with adequate extensions.
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Contributor : Laurent Massoulié <>
Submitted on : Monday, November 16, 2015 - 10:19:39 AM
Last modification on : Friday, April 30, 2021 - 9:52:17 AM



Laurent Massoulié, Alexandre Proutière, Mesrob Ohannessian. Greedy-Bayes for Targeted News Dissemination. SIGMETRICS '15 Proceedings of the 2015 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, Jun 2015, Portland, United States. pp.12, ⟨10.1145/2745844.2745868⟩. ⟨hal-01226779⟩



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