Search Results

Recommender systems help users to discover new products and services. The goal is generating meaningful recommendation to a collection of users with items or products that might interest them. Recommender systems are encountered on multiple domains such as e-commerce, content and media distribution, social media, and more. The course will cover fundamental and practical aspects of Recommender systems focusing on the data science approach. The course includes topics and concepts for recommender systems: collaborative filtering, content-based recommendation, knowledge-based recommendation, hybrid recommendation, attack-resistance recommendation, and evaluation of the recommender systems. Students will gain hands-on experiences with assignments and a term project.

College/Department: College of Computing and Informatics
Repeat Status: Not repeatable for credit
Prerequisites: DSCI 521 [Min Grade: C]