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.

翻译：本研究提出了一种利用智能体人工智能进行KYC（金融领域的"了解你的客户"）的前沿推荐系统，并在五个不同的内容垂直领域进行了评估：广告（Ad）、新闻、八卦、分享（用户生成内容）和技术（Tech）。该研究比较了四个实验组的性能，这些组根据KYC的密集使用程度进行划分，并以截断水平为$k=1$、$k=3$和$k=5$的归一化折损累计增益（nDCG）指标作为基准。通过将实验数据与理论框架以及来自百度、小红书等平台的行业基准相结合，本研究展示了构建大规模智能体推荐系统的实验结果，从而提供了深入的见解。