System Requirements Document (SRD)

Project Name: moon-movie

1. Introduction

The moon-movie project is a movie recommendation system designed to provide users with personalized movie suggestions using advanced machine learning techniques such as TF-IDF and cosine similarity. The system will feature a clean, responsive UI and will cater to users across various devices. Developed for Shantanu Dey in India, this project aims to deliver an engaging and intuitive experience for casual users and movie enthusiasts alike.

The system will leverage publicly available datasets like IMDB or MovieLens to ensure a rich variety of movies for recommendations. It will also include features such as user authentication, saving favorites, and rating movies to enhance personalization and interactivity.

2. System Overview

The moon-movie system will consist of the following core components:

1. Recommendation Engine: A content-based filtering system using TF-IDF and cosine similarity to analyze textual data such as genres, descriptions, and user reviews.
2. User Interface: A clean and responsive design optimized for accessibility across devices (desktop, tablet, and mobile).
3. Features:
   • User authentication (login/register).
   • Ability to save favorite movies and rate them.
   • Personalized recommendations based on user preferences.
4. Dataset Integration: Publicly available datasets like IMDB or MovieLens for movie information.

The system will be built using modern web technologies, ensuring scalability, reliability, and ease of maintenance.

3. Functional Requirements

Story Points:

• As a User, I should be able to register and log in to the system.
• As a User, I should be able to view personalized movie recommendations based on my preferences.
• As a User, I should be able to search for movies using keywords.
• As a User, I should be able to save movies to my favorites list.
• As a User, I should be able to rate movies and view ratings from other users.
• As a User, I should be able to access the system on any device with a responsive design.

4. User Personas

1. Casual User

• Description: A user who occasionally watches movies and seeks quick recommendations.
• Needs: Easy navigation, simple search functionality, and straightforward recommendations.

2. Movie Enthusiast

• Description: A user deeply interested in movies, genres, and ratings.
• Needs: Detailed recommendations, ability to rate and review movies, and access to curated lists.

3. Guest User

• Description: A user who has not registered but wants to explore the system.
• Needs: Limited access to recommendations and search functionality without saving preferences.

5. Visuals Colors and Theme

Color Palette:

• Background: #1A1A2E (Deep Midnight Blue)
• Surface: #16213E (Dark Navy)
• Text: #EAEAEA (Soft White)
• Accent: #0F4C75 (Vivid Teal)
• Muted Tones: #BBE1FA (Light Sky Blue)

This palette reflects a cinematic and immersive experience, with dark tones for a theater-like ambiance and vibrant accents for interactivity.

6. Signature Design Concept

Interactive Galaxy of Movies

The homepage will feature an interactive galaxy map where each star represents a movie. Users can navigate the galaxy by zooming and panning, clicking on stars to reveal movie details.

• Animations: Stars will twinkle subtly, and constellations will form dynamically based on genres or user preferences.
• Transitions: Smooth zoom and pan effects with hover interactions that highlight stars.
• Color Shifts: The galaxy background will subtly change colors based on the time of day (e.g., darker tones at night, lighter tones during the day).
• Micro-Interactions: Clicking a star will open a modal with movie details, including ratings, descriptions, and a "Save to Favorites" button.

This design concept ensures the homepage is visually captivating and encourages exploration, making the first impression unforgettable.

7. Non-Functional Requirements

• Performance: The system must load recommendations within 2 seconds.
• Scalability: Support up to 1 million users concurrently.
• Security: Implement secure authentication protocols and encrypt user data.
• Accessibility: Ensure compliance with WCAG 2.1 standards for accessibility.
• Localization: Default locale settings for India (IN), including timezone and currency formatting where applicable.

8. Tech Stack

Frontend:

• React for Web

Backend:

• Python
• FastAPI

Database:

• MySQL (preferred) or MariaDB for relational data
• Alembic for migrations

AI Models:

• GPT 5.4 for user-friendly responses
• Google Nano Banana for image generation

AI Tools:

• Litellm for LLM routing
• Langchain

Orchestration:

• Docker and docker-compose for local orchestration
• Kubernetes for server-side orchestration

9. Assumptions and Constraints

Assumptions:

• Publicly available datasets like IMDB or MovieLens will be used for movie information.
• Users will primarily access the system via web browsers and mobile devices.
• The recommendation engine will focus on textual data for content-based filtering.

Constraints:

• The system must adhere to Indian data protection laws.
• Limited budget for hosting and infrastructure.

10. Glossary

• TF-IDF: Term Frequency-Inverse Document Frequency, a statistical measure used to evaluate the importance of a word in a document relative to a corpus.
• Cosine Similarity: A metric used to measure how similar two vectors are, often used in recommendation systems.
• Modal: A pop-up window that overlays the main content, used for displaying additional information.
• WCAG: Web Content Accessibility Guidelines, standards for making web content accessible to people with disabilities.

This document outlines the requirements for the moon-movie project, ensuring clarity and alignment with Shantanu's vision.