Our team developed an innovative automated recommendation system for Craigslist, offering personalized product suggestions based on user searches. This technology prioritizes recommendations by factors like proximity, ad recency, and product condition. Initially focused on televisions in Indianapolis, the system is versatile for various products and locations. It enhances user engagement, strengthens Craigslist's market position, and transforms the platform into a user-centric hub. Leveraging advanced web scraping with Selenium, we collected data from Craigslist and Amazon—848 TV products and information on ten products, forming a robust foundation for analysis and model development.