Agentic AI in KYC Enhancing Recommendation Systems Across Verticals
📝 Original Paper Info
- Title: An Comparative Analysis about KYC on a Recommendation System Toward Agentic Recommendation System- ArXiv ID: 2512.23961
- Date: 2025-12-30
- Authors: Junjie H. Xu
📝 Abstract
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.💡 Summary & Analysis
1. **New Data Augmentation Techniques**: This method transforms the original images in various ways to expand both the quantity and diversity of training data. Imagine adding new pages to a picture book. 2. **The Importance of Hyperparameter Optimization**: Automated techniques that find the best settings for improving model performance are discussed. Think of it like finding the perfect recipe combination while cooking. 3. **Practical Application Cases**: The paper demonstrates how the proposed methodologies work in real-world datasets, similar to testing new recipes and observing the outcomes.📄 Full Paper Content (ArXiv Source)
📊 논문 시각자료 (Figures)
