Build a Hybrid Recommender System in Python using LightFM

Delivered personalized product suggestions based on user behavior and product features.

Enhanced the shopping experience by recommending products aligned with customer preferences.

Boosted user interaction and engagement with relevant product suggestions.

Led to increased product purchases by presenting users with more relevant recommendations.

Contributed to higher sales through targeted recommendations based on user segments and product features.

Developed a scalable recommendation system capable of handling large datasets efficiently.

Successfully combined collaborative and content-based filtering for more accurate recommendations.

Leveraged sparse matrices for efficient computation and memory usage in large datasets.

Addressed the cold start problem by using content-based filtering for new users and items.

Evaluated the system's performance with metrics like AUC, ensuring reliable recommendations.