论文标题
使用在线变分张量回归在社交网络上有针对性的广告
Targeted Advertising on Social Networks Using Online Variational Tensor Regression
论文作者
论文摘要
本文与社交网络上的在线有针对性广告有关。我们解决的主要技术任务是估计用户对的激活概率，这量化了一个用户对购买决策的影响可能对另一个用户产生的影响。这是一项艰巨的任务，因为一个营销事件通常涉及多种产品的不同产品的众多营销活动/策略。在本文中，我们提出了我们认为是第一个基于张量的在线广告上的基于张量的上下文强盗框架。所提出的框架旨在以多模式张量的形式适应任何数量的特征向量，从而使以统一的方式捕获与用户偏好，产品和广告系列策略可能存在的异质性。为了处理张量模式的相互依赖性，我们引入了具有平均场近似值的在线变分算法。我们从经验上证实，提出的tensorucb算法在影响基准上的影响最大化任务方面取得了重大改进，这归因于其捕获用户产品异质性的能力。
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.
