java-api

README fot the java-api.

Production and Development environment

• sudo docker ps (ensure no duplicate containers are running)
• sudo docker system prune --all --volumes (removes previous allocated cache)
• sudo docker compose -f docker-compose-db.yml up (starts only the database and pgadmin)
• sudo docker compose -f docker-compose-api.yml up (starts only the API server)

Fixes

• Read only error by changing file permissions: sudo chown -R $USER ./

Todo

• Implement RESTful API endpoints.
• Implement user authentication and authorization
• Implement security authentication and authorization
• Implement jwt bearer
• Implement refresh token
• Update docker-compose to V3 depends_on
• Implement server to server AMQP (Advanced Message Queuing Protocol)

Todo

• Implement RBAC
• Implement Logout.
• Implement Validation Annotations
• Implement helpers (done, info, warn, success, failure,)

User-Based Collaborative Filtering

Implementation Steps:

1. Collect (user_data, item_data, interaction_data)
2. Pre-process data
3. Similarity Calculation: measure the similarity between users, based on their preferences (get similar users)
4. Recommendation: get similar users and recommend items they liked, but the target hasnt interacted with (get_similar_users_preferences)

Item-Based Collaborative Filtering

Implementation Steps:

1. Collect data (user_data, movie_data, interaction_data)
2. Pre-process data
3. Similarity Calculation: Use a similarity metric to measure the similarity between items based on user interactions.
4. Recommendation: for a target item, identify similar items based on user interactions and recommend them to users who interacted with the target item.

Content-Based Filtering

Implementation Steps:

1. Collect data (user_data, item_data, interaction_data)
2. Pre-process data
3. Feature extraction: extract relevant features from the movie data (e.g., genre, keywords)
4. User-Profile Creation: create user profiles based on their preferences, incorporating explicit (ratings) and implicit (watch history) feedback.
5. Recommendation: recommend items that have features similar to the user's preferences.