Recognizance is an Instagram-based recommender system designed to address the challenges faced by T-Cup-Zone, a small food and milk tea business in Manila. This research focuses on developing a recommendation system using Naïve Bayes Classifier and Decision Tree to help them in expanding their business, identifying suitable locations for expansion, and enhancing visibility by suggesting potential influencers for product promotion. Recognizance aims to differentiate itself from traditional practices by leveraging Instagram as a platform for generating recommendations. The FURPS software quality model is utilized as the development and evaluation approach. A total of 50 respondents, consisting of CS/IT students from FEU Tech, professionals, and small business owners within Manila, evaluated the application. The assessment provided an average weighted mean of 4.5, indicating a high level of satisfaction. For users, Recognizance offers a valuable tool for small businesses aiming to enhance their marketing strategies and boost their competitiveness in the market. The proponents suggest that future researchers explore alternative social media platforms for data scraping to access a broader range of data sources, improving data quality and diversity. Additionally, the proponents recommend expanding the recommender system's categories to support a wider variety of small businesses.