nexREC: Graph-Based Movie Recommendation Engine 🎬

The Story Behind nexREC

nexREC was born out of a final-year academic project that took us on an incredible journey through the world of recommendation systems. What started as a simple idea evolved into a sophisticated graph-based recommendation engine that truly understands user preferences.

The Journey

The path to building nexREC wasn't straightforward. We began by exploring traditional recommendation systems, diving deep into content-based filtering and collaborative filtering. However, we quickly realized that user preferences are incredibly diverse and complex - influenced by social circles, trends, and personal experiences.

This realization led us to explore Graph Neural Networks (GNNs), a less-researched but promising area that could capture these complex relationships. After countless sleepless nights, hundreds of research papers, and numerous experiments, we developed a system that could instinctively respond to user needs.

Technical Implementation

Core Technologies

• Backend Framework: Django & Django Rest Framework
• Frontend: Remix
• Vector Database: Milvus
• Relational Database: PostgreSQL
• Machine Learning:
  • Sentence Transformers (SBERT)
  • PyTorch
  • PyTorch Geometric
• Deployment:
  • AWS Lightsail (Backend & Milvus)
  • Aiven Cloud (PostgreSQL)
  • HuggingFace (Models)
  • Vercel (Frontend)

Model Architecture

We experimented with various GNN architectures including:

• SAGEConv
• GAT (Graph Attention Networks)
• GATv2

After extensive hyperparameter tuning and optimization, we achieved an RMSE loss between 0.94 and 1.21, which is quite solid considering real-world rating deviations and data skewness.

Data Processing

The project involved processing approximately 10k movies with user reviews. We used PySpark for efficient data cleaning and processing, which was a significant challenge in itself. The data distribution analysis and sampling experiments were crucial in achieving our final results.

Team

This project wouldn't have been possible without the incredible teamwork of:

• Basanta Shrestha
• Pawan Shah
• Suwash Shrestha

Getting Started

Prerequisites

• Python 3.8+
• PostgreSQL
• Milvus

Acknowledgments

Special thanks to Sushan Kattel for the invaluable guidance in PySpark and data processing.

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Note: The deployed version is currently offline due to AWS billing considerations. We're working on making it available again soon!