CSCI 3312 Recommender Systems

Recommender systems have become an essential component of modern digital ecosystems, powering personalization across e-commerce platforms, music and video streaming services, social networks, and news portals. By filtering massive volumes of data, they help users discover relevant products and content, while enabling platforms to improve engagement and satisfaction. Recommender systems could also be applied in areas like travel, education, finance, and healthcare. Essentially, they could be valuable in situations where personalization supports decision-making and improves the user experience. This course provides a comprehensive exploration of recommender systems, covering their theoretical foundations, core algorithms, advanced models, and real-world applications. Beyond theoretical principles, students will gain hands-on experience in building and evaluating recommendation systems. The course also examines key practical challenges - including scalability, cold-start problems, and ethical considerations in personalized recommendations. Strong knowledge of Python and (CSCI2291 OR CSCI1102) AND (MATH2250OR MATH2210), or Instructor Permission.