ResearchRec: Research Article Recommendation Platform

ResearchRec is a platform that provides personalized research article recommendations by integrating advanced machine learning techniques like Active Learning and Transfer Learning. It ensures dynamic adaptation to user preferences and effective handling of cold-start scenarios.

View globally popular and engaging articles in a dynamic list.

Tailored research article suggestions based on user preferences and interactions.

Refine recommendations dynamically through user feedback and interactions.

Receive live updates on trending articles and personalized recommendations.

Incorporates NCF and Active Learning for enhanced recommendation accuracy.

This platform addresses the growing challenge of navigating the vast volume of scientific literature. It integrates advanced scrapping tools for metadata analysis, user behavior modeling, and generating research article recommendations.

The implementation begins with data preprocessing using techniques like TF-IDF vectorization and embeddings for extracting features from abstracts and tags.

Neural Collaborative Filtering (NCF) is employed to model complex interactions between users and articles. The Active Learning module enhances adaptability by iteratively refining the model with high-uncertainty data points, reducing dependency on large labeled datasets.

A hybrid scoring mechanism balances personalized recommendations with global article popularity, ensuring relevance and diversity. The application enables real-time updates on trends and incorporates user feedback loops for continuous improvement.