TrackEverything Webapp

Project Overview

TrackEverything is a comprehensive media tracking application that allows users to keep track of books, movies, TV shows, and games. It aims to provide personalized recommendations using AI based on users' watch lists and enable sharing of curated lists.

Current Progress

1. Created book and user databases
2. Established relations between databases (further clarification needed)
3. Implemented sign-up and sign-in error feedback
4. Next steps:
   • Clarify relationship between books and users
   • Develop profile page to display user's books

MVP (Minimum Viable Product) Features

1. Comprehensive list of books, movies, TV shows, and games
2. Media tracking functionality
3. User profile page
4. Intuitive and responsive UI

Future Enhancements

1. AI-powered recommendations
2. Social sharing feature with visually appealing top 10 lists
3. Optimized response time
4. User review system

Planned API Integrations

1. Movie Database API
2. Game Database API (IGDB)
3. Open Library API

Core Functionality

• Media tracking
• Updates on tracked media
• User comments
• Personalized recommendations

Recommendation System Features (Priority Order)

1. Mood-based Recommendations: Personalized suggestions based on user's current preferences and state of mind.
2. Adaptive Learning Path: Continuous improvement of recommendations through user interactions and feedback.
3. Cross-media Recommendations: Unique suggestions spanning different media types.
4. Recommendation Explanations: Transparent AI suggestions to build user trust and understanding.
5. Time-based Recommendations: Contextually relevant suggestions based on user's current lifestyle or habits.
6. Collaborative Filtering with Taste Clusters: Diverse recommendations based on similar user groups.
7. Interactive Story Building: Gamified approach to generating recommendations.
8. Social Reading Challenges: Engagement boosting feature to expand users' media consumption.
9. Contextual Recommendations: Suggestions based on current events or user's environment.
10. Gamified Recommendations: Engaging method for generating suggestions, though less suitable for users seeking more thoughtful processes.

TODO

• Finish comment and rating feature
• Implement remaining MVP features
• Develop and integrate AI recommendation system
• Create social sharing functionality
• Optimize performance and response time
• Implement user review system

Getting Started

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Contributing

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License

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