A collaborative filtering recommender system based on a new hybrid similarity measure and improved NSGA-II algorithm

Key Points

• The new hybrid similarity measure significantly improves recommendation accuracy in diverse datasets, enhancing user satisfaction.
• Integrating the improved NSGA-II algorithm, the system optimizes recommendations while processing large amounts of data efficiently.
• The proposed method focuses on collaborative filtering to better understand user preferences and deliver personalized suggestions.
• This work highlights the potential of combining algorithms to enhance recommender systems, addressing limitations in traditional approaches.