Add README.md to document the nexREC project, detailing its purpose, technical implementation, model architecture, data processing, team members, prerequisites, and acknowledgments.

Author: sudipnext

README.md

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