论社交网络中的个性化推荐
On personal recommendations in social networks
- KTH Royal Institute of Technology（皇家理工学院）
- Digital Futures（数字未来）
- WASP（瓦伦贝格人工智能、自主系统和软件项目）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一个解析可处理且符合确认偏误的个性化推荐模型，分析广播策略的双稳定性与定向策略的最优及鲁棒收敛，并通过数值模拟验证。
AI中文摘要：
算法通过个性化推荐主动影响人类的社交网络无处不在。虽然意见动力学是分析这些系统的成熟工具，但现有模型通常不捕捉个体智能体如何处理个性化推荐。在这项工作中，我们引入了一个在解析上可处理且与行为心理学中的确认偏误现象相一致的个性化推荐模型。我们描述了个体如何基于先验信念和敏感性参数，使用高斯影响函数来处理推荐。利用该模型，我们分析了不同推荐策略的效果。对于广播策略（即推荐在智能体之间是同质的），分岔分析表明系统表现出双稳定性，这可能导致意想不到的后果。对于个性化定向策略，我们首先推导出最优个性化推荐，然后扩展到当智能体对个性化推荐的敏感性不确定时的鲁棒收敛。所提出模型的集体行为及推导出的策略通过数值模拟进行了说明和评估。
英文摘要：
Social networks in which algorithms actively influence humans through personal recommendations are ubiquitous. While opinion dynamics is an established tool to analyze these systems, existing models typically do not capture how individual agents process personal recommendations. In this work, we introduce a model for personal recommendations that is analytically tractable and consistent with the confirmation bias phenomenon from behavioral psychology. We describe how individuals process recommendations based on prior beliefs and a sensitivity parameter using a Gaussian influence function. Using this model, we analyze the effect of different recommendation policies. For broadcast policies, where recommendations are homogeneous across the agents, a bifurcation analysis shows that the system exhibits bistability, which may result in unintended consequences. For personally targeted policies, we first derive optimal personal recommendations and then extend to robust convergence when the agents' sensitivity to personal recommendations is uncertain. The collective behavior of the proposed model and the derived policies are illustrated and evaluated through numerical simulations.
