  This paper is concerned with online targeted advertising on social networks.
The main technical task we address is to estimate the activation probability
for user pairs, which quantifies the influence one user may have on another
towards purchasing decisions. This is a challenging task because one marketing
episode typically involves a multitude of marketing campaigns/strategies of
different products for highly diverse customers. In this paper, we propose what
we believe is the first tensor-based contextual bandit framework for online
targeted advertising. The proposed framework is designed to accommodate any
number of feature vectors in the form of multi-mode tensor, thereby enabling to
capture the heterogeneity that may exist over user preferences, products, and
campaign strategies in a unified manner. To handle inter-dependency of tensor
modes, we introduce an online variational algorithm with a mean-field
approximation. We empirically confirm that the proposed TensorUCB algorithm
achieves a significant improvement in influence maximization tasks over the
benchmarks, which is attributable to its capability of capturing the
user-product heterogeneity.
