User Registration and Authentication: Allow users to register, authenticate, and manage their profiles securely.
Profile Data: Collect and store user preferences, interactions, and ratings (e.g., products, movies, music).
Recommendation Algorithms
User-Based Collaborative Filtering: Implement algorithms that recommend items based on similar users’ preferences (e.g., nearest neighbor methods).
Item-Based Collaborative Filtering: Implement algorithms that recommend items based on the similarity between items (e.g., item-item similarity).
Data Collection and Storage
Interaction Data: Collect and store user interactions with items, such as ratings, clicks, purchases, and views.
Historical Data: Maintain historical data of user interactions for building and updating recommendation models.
Personalized Recommendations
Recommendation Generation: Generate personalized recommendations for users based on their profiles and interaction history.
Recommendation Display: Display recommendations in a user-friendly format, such as lists, grids, or carousels.
Real-Time Updates
Dynamic Recommendations: Update recommendations in real-time or near real-time as users interact with the system.
Adaptation: Adjust recommendations based on new user interactions and changing preferences.
Evaluation and Feedback
Feedback Collection: Allow users to provide feedback on recommendations (e.g., thumbs up/down, ratings).
Performance Metrics: Track and evaluate the performance of recommendation algorithms using metrics such as precision, recall, and user satisfaction.
Integration
Third-Party Integration: Integrate with external data sources, such as social media or external databases, to enhance recommendations.
API Support: Provide APIs for integrating recommendation services with other applications or platforms.
User Interface
User Interaction: Design an intuitive interface for users to view recommendations and interact with them.
Customization: Allow users to customize their recommendation settings or preferences.
Scalability
Data Handling: Support large-scale data processing to handle a growing number of users and items.
Algorithm Efficiency: Ensure algorithms are efficient and scalable to accommodate increasing data and user base.
Non-Functional Requirements
Performance
Response Time: Ensure low latency for generating and displaying recommendations to provide a responsive user experience.
Throughput: Handle a high volume of user interactions and recommendations efficiently.
Usability
User Experience: Provide an intuitive and user-friendly interface that enhances user engagement and satisfaction.
Accessibility: Ensure the system is accessible to users with disabilities, adhering to accessibility standards.
Reliability
System Availability: Maintain high availability with minimal downtime to ensure continuous access to recommendations.
Error Handling: Implement robust error handling to manage and recover from failures or unexpected issues.
Security
Data Privacy: Protect user data and interactions from unauthorized access or breaches, ensuring compliance with privacy regulations.
Authentication and Authorization: Implement strong authentication and authorization mechanisms to secure user accounts and data.
Maintainability
Code Quality: Maintain a clean, well-documented, and modular codebase to facilitate updates and maintenance.
Update Management: Provide a structured process for deploying updates and improvements to the recommendation algorithms and system components.
Compatibility
Platform Support: Ensure compatibility with various platforms and devices, including web and mobile applications.
Integration: Support integration with different data sources and third-party services.
Compliance
Regulatory Compliance: Adhere to relevant regulations and standards, such as data protection laws (e.g., GDPR, CCPA), for handling user data and privacy.
Industry Standards: Follow industry best practices for recommendation systems and collaborative filtering algorithms.
Scalability
Data Scalability: Support scalability to handle increasing amounts of data and user interactions.
Algorithm Scalability: Ensure that recommendation algorithms can scale efficiently with growing user and item datasets.
