About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
KAIS
Paper
Sequential uncertainty quantification with contextual tensors for social targeting
Abstract
A common question that an online marketer asks is, given a social network, how one decides which users to target with their limited advertisement resources (such as coupon offers). The key technical task toward answering this question is the estimation of the user–user activation probability that quantifies the influence one user may have on another toward their purchasing decisions. In this paper, we propose a novel targeting strategy with sequential uncertainty quantification via probabilistic tensor regression. The proposed framework is designed to capture the heterogeneity in user preferences, product types, campaign strategies, etc. in the form of contextual tensor. For uncertainty quantification, we derive a closed-form online predictive distribution for the user response score, which is used in a bandit-style sequential decision-making on who to receive marketing offers. We empirically confirm that the proposed algorithm achieves significant improvement in influence maximization tasks over benchmarks, which is attributable to its capability of capturing the user–product heterogeneity.