Next-Gen Recommendation System

A custom recommendation engine inspired by platforms like YouTube and Google Search, designed to provide personalized content recommendations using AI and machine learning.

Screenshots

Home Page

User-friendly interface showcasing recommended content.

Search and Recommendations

When users search or interact with content, related topics appear in real-time for seamless navigation.

Video Player with Related Suggestions

Video player interface with recommended videos tailored to user preferences.

About

This Custom Recommendation System project is built to deliver user-specific content recommendations. Using vector embeddings, language models, and real-time data storage, it provides a highly personalized experience that mirrors leading platforms. This project combines frontend efficiency with the power of AI to showcase an intuitive, scalable recommendation engine.

Tech Stack

• Next.js with TypeScript: For building a high-performance, scalable frontend.
• Convex DB: To handle vector embeddings and real-time data storage.
• Voyage AI: Provides AI-driven recommendation algorithms.
• LangChain.js: Facilitates the integration and management of language models (LLMs).
• Tailwind CSS: Ensures a responsive and visually appealing design.
• Vercel: Seamless deployment platform for scalable web applications.

Features

• Dynamic Content Recommendations: Uses AI and vector embeddings to analyze user interactions and provide recommendations for similar or related content in real-time.

• Contextual Search Optimization: Personalized search results improve over time based on user preferences, delivering more relevant recommendations.

• Intelligent Video Queue: Automatically queues up similar content based on the currently viewed video or search, giving users a seamless viewing experience.

• Customizable with Convex DB and LangChain.js: Configurable to support various data sources and language models, making the recommendation system flexible for different use cases.

• Smooth Integration with Voyage AI: Utilizes Voyage AI algorithms for complex recommendation models, offering powerful insights and adaptability.

• Responsive and Modern UI with Tailwind CSS: Provides a sleek, intuitive interface optimized for both desktop and mobile views, ensuring an engaging user experience across devices.

What I Learned

• AI-Driven Recommendation Systems: Gained hands-on experience in building recommendation engines, understanding how vector embeddings and language models can drive personalized content.

• Integration of LLMs with LangChain.js: Learned how to manage and integrate language models effectively to support content recommendations.

• Data Handling with Convex DB: Understood how to utilize Convex DB for real-time data handling and vector embeddings, crucial for creating fast and reliable recommendations.

• Frontend Development with Next.js and TypeScript: Enhanced my knowledge of Next.js for building server-rendered applications and using TypeScript to enforce type safety across the project.

• UI Design with Tailwind CSS: Improved my ability to design responsive, modern UIs, making the user interface engaging and visually appealing.

Usage

Once launched, the recommendation system can be customized for various content types by adjusting the settings in the LangChain.js and Convex DB integrations. It is designed to provide real-time recommendations based on user interactions.