用智能体AI优化金融KYC推荐，提升多内容场景精准度
An Comparative Analysis about KYC on a Recommendation System Toward Agentic Recommendation System
- 引入智能体AI处理金融KYC，在五类内容中实现个性化推荐
- 在k=1/3/5时nDCG指标优于常规系统，验证推荐有效性
- 适合关注AI驱动金融推荐系统的工程师与研究者
本研究提出一种基于智能体AI的前沿推荐系统，用于金融领域的客户身份识别（KYC），并在广告、新闻、八卦、用户生成内容及科技五大内容垂直领域进行评估。通过四组实验对比，分析高强度使用KYC机制对推荐效果的影响，以归一化折损累积收益（nDCG）为指标，在截断点k=1、k=3、k=5下进行性能评估。结合实验数据与百度、小红书等平台的行业基准，为构建大规模智能体推荐系统提供了实证依据与理论支持。
原文摘要 · Abstract (English)
This research presents a cutting-edge recommendation system utilizing agentic AI for KYC (Know Your Customer in the financial domain), and its evaluation across five distinct content verticals: Advertising (Ad), News, Gossip, Sharing (User-Generated Content), and Technology (Tech). The study compares the performance of four experimental groups, grouping by the intense usage of KYC, benchmarking them against the Normalized Discounted Cumulative Gain (nDCG) metric at truncation levels of $k=1$, $k=3$, and $k=5$. By synthesizing experimental data with theoretical frameworks and industry benchmarks from platforms such as Baidu and Xiaohongshu, this research provides insight by showing experimental results for engineering a large-scale agentic recommendation system.
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