04-761 Recommender Systems
12 units, Carnegie Mellon University
Information overload is a characteristic of our society today. This has made decision-making more complicated than ever before. For example, choosing which restaurant to go to for the day’s meal is further complicated by a variety of dishes on the same menu. In the entertainment industry, one has to choose whether to read a book or watch a movie and, if so, which one. Recommender systems have come in handy as far as overcoming such challenges is concerned. Recommender systems are now largely used, particularly in eCommerce websites, for easing the information search and discovery processes and increasing customer fidelity and conversion rates. They attempt to guide people into making decisions based on their preferences and personalities and by mimicking the choices of people similar to them. Recommendation systems are part of machine learning or artificial intelligence (AI) algorithms. Students should gain basic knowledge about the field of web recommendation systems and other main recommender systems methods and tools as well as their theoretical foundations. The students will familiarize themselves with the concept of recommender systems and the scientific underpinnings of the latter in different domains. Students will also gain the necessary tools and techniques to analyze visual and statistical data, build models, and present findings to support data-driven decisions as data science engineers. Students will be involved in hands-on projects such as choosing a topic to work on and getting corresponding data and sources. For example, students can choose from recommender systems in transformer-banking products, healthcare, restaurants, fashion, books, movies, music, and more. For the final project, students will work on RecSys challenges organized by members of the ACM Conference on Recommender Systems.
Prerequisites: none listed
Courseweave needs JavaScript to show sections, workloads and planning tools.