Recommender Systems and Techniques

Recommender Systems and Techniques is a research topic within Information Systems. Science Explorer counts 25k research works in it since 1962. 9.8% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the advancements in recommender system technologies, including collaborative filtering, matrix factorization, deep learning, content-based recommendation, web mining, context-aware recommender systems, neural networks, user modeling, and trust-aware recommender systems. The papers cover various techniques and methodologies for improving recommendation accuracy and addressing challenges such as cold start problems and privacy concerns.

• Collaborative Filtering
• Matrix Factorization
• Deep Learning
• Content-Based Recommendation
• Web Mining
• Context-Aware Recommender Systems
• Neural Networks
• User Modeling
• Trust-Aware Recommender Systems
• Click-Through Rate Prediction