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DSCI 351 Recommender Systems 3.0 Credits

Recommender systems are electronic commerce information filtering systems to predict items that the users may have interest in. The goal of this course is to provide an overview of recommender systems, including content-based and collaborative algorithms for recommendation, programming of recommender systems, and evaluation and metrics for recommender systems. The course introduces all relevant topics of Recommender Systems: overview, non-personalized recommendation, content-based recommending, neighborhood-based collaborative filtering, recommender system evaluation and advanced topics. 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: INFO 212 [Min Grade: D]

Economics and Data Science

http://catalog.drexel.edu/undergraduate/schoolofeconomics/economicsanddatascience/

...Credits COOP EXPERIENCE COOP EXPERIENCE DSCI 351 3.0 DSCI 471 3.0 INFO 323...

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