This project involves building a sophisticated recommendation system that personalizes user experiences by suggesting relevant products, content, or services based on user behavior and preferences. The platform aims to enhance user engagement and satisfaction by providing tailored recommendations across various domains, such as e-commerce, streaming, or content platforms.
The project aims to develop a scalable and efficient API that processes user data, analyzes patterns, and generates personalized recommendations. This API will support various front-end applications, including web and mobile interfaces.
In today's digital landscape, personalized recommendations are crucial for driving user engagement and increasing conversion rates. This project will create an API that empowers users with relevant suggestions while allowing administrators to manage recommendation algorithms and user data effectively.
User Interaction Overview
User Registration and Authentication
Sign Up: New users can create an account by providing their email and password confirmation email will be sent for account verification
Login: Registered users can log in using their email and password. The API will support multi-factor authentication (MFA) for added security.
Profile Management
View and Edit Profile: Users can view and update their profile details, including preferences and interaction history to enhance the personalization of
recommendations.
Recommendation Generation
Personalized Suggestions: Users receive tailored product or content suggestions based on their behavior, preferences, and interactions.
Trending Recommendations: Users can view popular recommendations based on current trends.
Feedback Mechanism
Rate Recommendations: Users can provide feedback on recommendations to improve accuracy and relevance over time.
Objectives
Allow users to sign up, log in, and manage their accounts securely.
Enable personalized recommendations based on user behavior and preferences.
Provide a feedback mechanism to refine and improve recommendations.
Offer trending recommendations based on real-time data analysis.
Ensure robust security and user data protection.
Functional Requirements
User Management
Sign Up: Users can create an account using their email and password.
Login: Users can authenticate using their email and password.
Profile Management: Users can update their profile and preferences.
Recommendation Generation
Get Recommendations: Users can retrieve personalized recommendations based on their interaction history.
Get Trending Recommendations: Users can access recommendations that are currently popular among other users.
Feedback Mechanism
Rate Recommendations: Users can provide ratings for the suggestions they receive.
Get Trending Recommendations: Users can access recommendations that are currently popular among other users.
Non-Functional Requirements
Scalability: The API should handle a growing number of users and interactions without performance degradation.
Performance: The API should deliver fast response times for recommendation retrieval.
Security: Implement authentication and authorization mechanisms to protect user data.
Reliability: The API should be highly available and resilient to failures.
Usability: The API should be user-friendly and well-documented.
Use Cases
User Sign Up and Login: New users create an account, and existing users log in.
Retrieve Recommendations: Users request personalized product or content recommendations.
View Trending Recommendations: Users access popular recommendations.
Provide Feedback: Users rate recommendations to enhance the system.
User Stories
As a user, I want to sign up for an account so that I can receive personalized recommendations.
As a user, I want to log in to my account to manage my preferences.
As a user, I want to receive recommendations based on my past interactions and preferences.
As a user, I want to view trending recommendations to discover popular items.
As a user, I want to provide feedback on recommendations to help improve the system.
Technical Requirements
Programming Language: Choose an appropriate backend language (e.g., Python, Node.js).
Machine Learning Framework: Utilize a framework (e.g., TensorFlow, scikit-learn) for implementing recommendation algorithms.
Database: Use a database to store user data, interactions, and recommendation models (e.g., PostgreSQL, MongoDB).
Authentication: Implement JWT for secure user authentication.
API Documentation: Use Swagger or similar tools for API documentation.
API Endpoints
User Management
POST /signup: Register a new user.
POST /login: Authenticate a user.
GET /profile: Retrieve user profile details.
PUT /profile: Update user profile.
Recommendation Generation
GET /recommendations: Retrieve personalized recommendations.
GET /recommendations/trending: Access trending recommendations
Feedback Mechanism
POST /feedback: Submit ratings for recommendations.
Security
Use HTTPS to encrypt data in transit.
Implement input validation and sanitization to prevent security vulnerabilities.
Use strong password hashing algorithms like bcrypt.
Performance
Implement caching strategies to improve response times for frequent recommendation requests.
Optimize algorithms for efficient processing of large datasets.
Documentation
Provide comprehensive API documentation using tools like Swagger.
Create user guides and developer documentation to assist with integration and usage.
Glossary
API: Application Programming Interface.
MFA: Multi-Factor Authentication.
ML: Machine Learning.
Appendix
Include any relevant diagrams, data models, and additional references.
