Course Description:
This course covers various concepts of recommender systems, including personalization algorithms, evaluation tools, and user experiences. Discussion of how recommender systems are deployed in business applications, design of new recommender experiences, and how to conduct and evaluate research in recommender systems. Cannot take for credit if already took CS 4379Q.Prerequisite:
- C or higher in CS 3358: Data Structures -OR-
- Consent of Instructor
Course Objectives:
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1. |
Describe different approaches to recommendation |
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2. |
Infer the recommendation technologies likely used in an application |
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3. |
Build a basic recommender algorithm |
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4. |
Interpret evaluation results to select and optimize a recommender algorithm |
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5. |
Design a recommender-based application (select data sources and types, likely-appropriate algorithms, recommendation presentation style, etc.) |
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6. |
Recognize and list various and competing concerns in recommender development (business, legal, privacy, security, user interest) |
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7. |
Identify and explain user's goals and needs for a recommender application |
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8. |
Synthesize and/or reproduce results from recent research papers |
Course Notes:
New course effective Spring 2015.
Section Info:
Lecture/Lab Hours: 3 hours lecture
Offered: