Problem Statement:

Currently, the platform relies on manual matching between students/researchers and relevant projects/stakeholders. This process can be time-consuming and inefficient, especially as the platform grows.

Proposed Solution:

Implement an AI-powered automated paper-matching system that leverages advanced algorithms to analyze user profiles, project descriptions, and research papers. The system should be able to:

1. Profile Analysis: Accurately extract keywords, skills, and interests from user profiles and research papers.
2. Semantic Matching: Utilize semantic similarity techniques to identify relevant connections between users and projects.
3. Personalized Recommendations: Provide tailored recommendations to users based on their preferences and expertise.
4. Continuous Learning: Continuously learn and improve matching accuracy over time by analyzing user feedback and interaction data.

Technical Considerations:

• AI Algorithm Selection: Research and select appropriate AI algorithms, such as natural language processing (NLP), machine learning, or deep learning techniques.
• Data Preprocessing: Clean and preprocess user data, research papers, and project descriptions to ensure accurate analysis.
• Feature Engineering: Extract relevant features from the data, including keywords, skills, and semantic relationships.
• Model Training and Evaluation: Train and evaluate the AI model on a labeled dataset to optimize performance.
• User Interface Integration: Integrate the automated matching system into the platform's user interface, providing a seamless user experience.

Acceptance Criteria:

• The system should accurately match users with relevant projects and stakeholders.
• The system should provide personalized recommendations to users.
• The system should be scalable to handle a growing number of users and data.
• The system should be user-friendly and easy to use.

Additional Notes:

• Consider using a combination of techniques, such as keyword matching, semantic similarity, and collaborative filtering, to improve matching accuracy.
• Incorporate user feedback to refine the recommendations and improve the system's performance over time.
• Regularly evaluate the system's performance and make necessary adjustments.

By implementing this feature, we can significantly enhance the platform's efficiency and effectiveness, making it easier for users to find relevant opportunities and collaborate on research projects.