Give Choices Back to User: A Personalized Movie Recommendation Software with User-Selectable Algorithms and Dynamic Hybrid Optimization

By Fei PAN, Kaixi HAO, Xiaomeng SHI

Highlights

• Personalized content recommendation integrating Collaborative filtering and Content-based filtering
• Hybrid algorithm approach addressing the cold-start problem and preventing recommendation homogeneity, boosting diversity and accuracy
• Continuous optimization of user model based on real-time behavior and evolving preferences
• Transparent and explainable recommendations fostering user trust and satisfaction

Applications

• Multimodal entertainment content recommendations such as news, music, books, and TV shows
• E-commerce platforms delivering personalized product suggestions
• Advertising agencies enabling targeted and relevant ad placements