New Bandit Framework Optimizes Social Network Word-of-Mouth

A new research paper introduces a contextual multi-armed bandit framework designed to optimize stimulated word-of-mouth strategies. The framework learns individual spillover probabilities among users in social networks to identify and target those most susceptible to information sharing. Experiments on real-world datasets show that this approach improves targeting precision and boosts rewards compared to methods that do not account for spillover heterogeneity. AI

IMPACT This research could lead to more effective viral marketing and information dissemination strategies in online social networks.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new framework for optimizing word-of-mouth rewards. [lever_c_demoted from research: ic=1 ai=0.7]

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• Ahmed Sayeed Faruk
• alphaXiv
• arXiv
• CatalyzeX
• Contextual Bandits for Maximizing Stimulated Word-of-Mouth Rewards
• DagsHub
• Gotit.pub
• Hugging Face
• IArxiv
• ScienceCast

• paper
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