arXiv:2510.19119v2 Announce Type: replace Abstract: In networked environments, it is common for users to share recommendations about content, products, services, and possible courses of action. Whether these recommendations are accepted and acted upon is highly context-dependent, influenced by the characteristics of the sender and recipient, the nature of their relationship, the attributes of the recommended item, and the communication context. Consequently, probabilities of peer influence exhibit substantial heterogeneity across individuals and settings. Accurate estimation of these probabili
The proliferation of networked environments and the advancement of AI and machine learning techniques enable more sophisticated modeling of human interaction dynamics.
Accurate estimation of peer influence is critical for optimizing recommendation systems, political campaigns, marketing strategies, and understanding social contagion.
The ability to quantify and predict how context influences peer recommendations introduces a new layer of precision to social influence modeling, moving beyond simple network propagation.
- · Social media platforms
- · Advertising and marketing firms
- · AI/ML researchers
- · E-commerce companies
- · Traditional, undifferentiated advertising
- · Organizations relying on aggregate influence metrics
Recommendation engines become significantly more personalized and effective, leveraging granular contextual data.
Political campaigns and social movements could gain more precise tools for targeting and swaying public opinion.
The enhanced predictability of social influence might lead to new ethical debates around manipulation and algorithmic transparency.
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