Recommendation Quality

Recommendation quality focuses on improving the accuracy, relevance, and fairness of systems that suggest items to users. Current research emphasizes mitigating biases (e.g., popularity bias), enhancing scalability for large datasets, and improving the explainability and trustworthiness of recommendations through techniques like incorporating knowledge graphs, large language models (LLMs), and advanced algorithms such as graph neural networks and matrix factorization. These advancements are crucial for enhancing user experience and addressing ethical concerns in various applications, from e-commerce and social media to personalized healthcare and education.