用AI推荐激发社交网络创意，让灵感更分散更出彩。
AI Can Enhance Creativity in Social Networks
- 基于语义与网络结构预测创意表现，生成个性化推荐
- 使用AI推荐的网络创意表现优于传统方法，且更去中心化
- 适合设计智能创意激励系统的研究者和产品团队
在自组织社交网络中，同行推荐引擎能否提升人们的创造性表现？这一问题需克服数据收集（如追踪灵感来源与心理社会属性）与干预设计（如平衡创意刺激与冗余）的挑战。我们训练了一个模型，利用在线平台中的语义与网络结构特征预测用户创意表现。基于该模型构建了SocialMuse系统，通过最大化预测表现来生成同行推荐。实验发现，采用SocialMuse的处理网络在多项创意指标上优于无AI干预的对照组。随着网络规模增大，SocialMuse愈发强调网络结构特征，使处理网络更具去中心化特征，从而拓宽个体灵感来源，帮助创意脱颖而出。研究为构建提升创造力的智能系统提供了可操作洞见。
原文摘要 · Abstract (English)
Can peer recommendation engines elevate people's creative performances in self-organizing social networks? Answering this question requires resolving challenges in data collection (e.g., tracing inspiration links and psycho-social attributes of nodes) and intervention design (e.g., balancing idea stimulation and redundancy in evolving information environments). We trained a model that predicts people's ideation performances using semantic and network-structural features in an online platform. Using this model, we built SocialMuse, which maximizes people's predicted performances to generate peer recommendations for them. We found treatment networks leveraging SocialMuse outperforming AI-agnostic control networks in several creativity measures. The treatment networks were more decentralized than the control, as SocialMuse increasingly emphasized network-structural features at large network sizes. This decentralization spreads people's inspiration sources, helping inspired ideas stand out better. Our study provides actionable insights into building intelligent systems for elevating creativity.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。