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.

翻译：本文涉及社交网络的在线定向广告。我们处理的主要技术任务是估计用户对口的激活概率,以量化用户对另一用户在购买决定方面可能产生的影响。这是一项艰巨的任务,因为一个营销事件通常涉及为高度多样化的客户开展多种不同产品营销运动/战略。在本文中,我们提出我们认为是在线定向广告的第一个基于温度的背景强盗框架。拟议框架旨在容纳以多模多色度形式出现的任何数量的特点矢量,从而能够以统一的方式捕捉到用户偏好、产品和运动战略可能存在的异质。为了处理高温模式的相互依存性,我们引入了一种具有中值近似性的在线变异算法。我们从经验上确认,拟议的TensorUCB算法在对基准影响最大化任务上取得了显著的改进,这归功于它捕捉用户产品异质的能力。