[25] Develop an AI-powered content recommendation system

[daaimah123]

Problem: We need an intelligent system to recommend relevant courses, content, and learning paths to participants based on their interests, performance, and goals.

Implementation Details:

1. Design and implement a recommendation engine using machine learning techniques
2. Develop data collection and preprocessing pipelines for user behavior and preferences
3. Create a content tagging and categorization system
4. Implement collaborative filtering algorithms for finding similar users and content
5. Develop a hybrid recommendation system combining content-based and collaborative filtering
6. Create an A/B testing framework for evaluating recommendation effectiveness
7. Implement an explainable AI component to provide reasons for recommendations

Technical Concepts:

• Recommendation system algorithms (collaborative filtering, content-based filtering)
• Machine learning model design and training
• Natural language processing for content analysis
• A/B testing methodologies
• Explainable AI techniques

Dependencies: Issues 19, 20, and 21

Acceptance Criteria:

• Recommendation engine provides personalized and relevant content suggestions
• Data collection accurately captures user behavior and preferences
• Content tagging system effectively categorizes and describes available content
• Collaborative filtering accurately identifies similar users and content
• Hybrid recommendation system outperforms individual approaches
• A/B testing framework allows for continuous improvement of recommendations
• Users can understand the reasons behind recommendations through explainable AI component