通过学习用户间传播差异，精准挑选最易引发口碑传播的社交节点。
Contextual Bandits for Maximizing Stimulated Word-of-Mouth Rewards
- 基于上下文多臂赌博机模型，动态估计个体传播概率。
- 实验证明考虑传播异质性可提升前k名目标用户的定位精度。
- 适合社交营销、精准推广等需要最大化口碑效应的场景。
刺激式口碑传播是通过提示或激励促进信息分享的策略。在社交网络中优化该策略，需识别并靶向那些最易产生影响扩散（即推荐影响超出直接受众，波及关联用户）的连通用户。不同个体及其连接关系的扩散概率存在异质性，准确估计这种差异对提升传播效果至关重要。为此，我们提出一种新型上下文多臂赌博机框架，用于学习个体扩散概率，并排序连通用户以最大化刺激式口碑奖励。在真实社交网络数据集上的实验表明，考虑扩散异质性显著提升了前k名连通用户的定位精度，从而增加奖励，优于未学习个体扩散效应的基线方法。
原文摘要 · Abstract (English)
Stimulated word-of-mouth is a strategy that promotes information sharing through prompts or incentives. Optimizing stimulated word-of-mouth through social networks requires identifying and targeting connected users who are most susceptible to spillover, a phenomenon where the influence of recommendations extends beyond the immediate audience to impact their connected users. The probability of spillover varies across individuals, and their connections, leading to heterogeneity. Understanding and accurately estimating the spillover probabilities among users in social networks is crucial for improving the effectiveness of stimulated word-of-mouth. To address this, we present a novel contextual multi-armed bandit framework that learns individual spillover probabilities and ranks connected users to maximize rewards from stimulated word-of-mouth. Experiments on real-world network datasets demonstrate that accounting for spillover heterogeneity enhances the targeting precision of top-$k$ connected users, boosting rewards and outperforming baseline methods that do not learn individual spillover effects.
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