Case Study Recommenu
About Project
Recommenu is an AI-driven food discovery app designed to go beyond generic restaurant searches by recommending both where to eat and what to order. The app builds a personalized User Taste Profile for each individual, learning their flavor preferences and predicting how strongly they might like a particular dish. Recommendations are not limited to individuals—Recommenu also addresses the common challenge of group dining by analyzing multiple taste profiles (e.g., friends, coworkers, or teams) and suggesting restaurants with menu items that maximize collective satisfaction. The goal was to create a seamless, data-powered solution that personalizes dining experiences at both the individual and group level.
Client Requirements
The project required a solution that could capture and analyze user taste data from diverse sources, build a robust recommendation engine capable of predicting restaurant and menu preferences, and support group dining decisions by merging multiple profiles into optimized results. Additionally, it was essential to automate restaurant and menu data collection at scale, leverage advanced AI and machine learning technologies for accuracy, and deliver everything within a scalable, user-friendly application framework.
Solutions We Delivered
To address these needs, we built a complete recommendation ecosystem powered by modern AI and machine learning. We began by implementing web scraping with ScraperAI and ScraperAPI to gather restaurant and menu data at scale. The raw data was then structured and enriched using ChatGPT, which also served as an alternate recommendation logic layer when additional insights were needed. At the core of the platform, we developed a sophisticated Recommendation Engine in Python, combining multiple technologies to ensure precision and scalability. Pinecone was integrated as a vector database to handle similarity searches and user profile matching, while Google Vertex AI provided the infrastructure for training and deploying machine learning models. For advanced personalization and prediction, we utilized Google Gemini AI, enabling the system to adapt dynamically to evolving user preferences. This integrated approach allowed us to create a personalized, intelligent, and scalable recommendation system that not only offers highly relevant restaurant and menu suggestions but also optimizes dining choices for groups. The result is a platform that transforms everyday dining decisions into data-driven experiences tailored to individual and collective tastes.
Client
Project Links
Country
US
Technologies we used
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MongoDB
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Node
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Python
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React Native
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