Contextual Bandits for Maximizing Stimulated Word-of-Mouth Rewards

2026-06-13 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

A novel contextual multi-armed bandit framework is introduced to optimize stimulated word-of-mouth (WOM) strategies in social networks. This framework addresses the challenge of identifying and targeting connected users most susceptible to "spillover," where influence extends beyond immediate recipients. Recognizing that spillover probabilities vary significantly across individuals and their connections, the system learns these individual probabilities and ranks connected users to maximize rewards from WOM campaigns. Experiments conducted on real-world network datasets demonstrate that explicitly accounting for this spillover heterogeneity significantly enhances the targeting precision of top-$k$ connected users. This approach boosts overall rewards and consistently outperforms baseline methods that do not incorporate individual spillover effects.

Key takeaway

For marketing strategists or data scientists designing social network campaigns, understanding and leveraging individual spillover probabilities is crucial. Your campaigns can achieve significantly higher rewards by implementing models that account for user-specific influence heterogeneity, rather than relying on generalized targeting. Consider adopting contextual bandit approaches to dynamically learn and rank users for stimulated word-of-mouth initiatives, boosting targeting precision and overall campaign effectiveness.

Key insights

A contextual multi-armed bandit framework optimizes stimulated word-of-mouth by learning individual spillover probabilities in social networks.

Principles

• Spillover heterogeneity impacts WOM effectiveness.
• Learning individual spillover probabilities improves targeting.
• Contextual bandits can maximize WOM rewards.

Method

A contextual multi-armed bandit framework learns individual spillover probabilities and ranks connected users to maximize stimulated word-of-mouth rewards in social networks.

In practice

• Target users based on individual spillover potential.
• Optimize social network marketing campaigns.

Topics

• Contextual Bandits
• Word-of-Mouth Marketing
• Social Network Analysis
• Spillover Effects
• User Targeting
• Machine Learning

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.