SI 583 Recommender Systems
Instructor: Rahul Sami
1.5 Credit, 7-week course module
First half of Winter 2008
Tuesday, Thursday 10:30am-12:00noon, 409WH
Office hours: Monday 3-4PM 3246ESI-N; Thursday 2-3PM 314WH
Course Goal:
In this course, you will learn about the design of recommender systems:
the underlying concepts, design space, and tradeoffs. At the end of this
course, a student should understand the design space of recommender systems and be able to provide design recommendations for a particular application domain,
as well as critique a design to point out its strengths and weaknesses.
Overview:
Recommender systems guide people to interesting materials based on information from other people. There is a large design space of alternative ways to organize such systems. The information that other people provide may come from explicit ratings, tags, or reviews, or may be implicitly inferred from their browsing, linking, or buying behavior. This information can be aggregated and used to select, filter, sort, or highlight items. The recommendations may be personalized to the preferences of different users.
In this course, we will study the design and critical analysis of
recommender systems. We will discuss incentive issues involved in
motivating users to behave honestly and to give honest feedback, as well as
other practical aspects of designing a reputation system, such as the format of feedback input and retrieval. We will also study ways in which
strategic parties may try to circumvent the system, and techniques to defend against these attacks.
Prerequisites
- An introduction to statistics (SI544 or equivalent) or permission of
instructor. We will be using matrix algebra, but the necessary material for
that will be covered in the lectures
Course Schedule
- Week 1:
Introduction to the recommender systems design space
Eliciting Ratings and other Feedback Contributions
- Week 2:
Implicit Ratings
e-Commerce Applications
- Week 3:
Linear Algebra: Matrix addition, multiplication, transposition, and inverses; covariance matrices
User-User Recommender Algorithm
- Week 4:
Case Study: Recommending Messages in an Online Community
Item-Item Recommender Algorithm
- Week 5:
Matrix-based Recommender Algorithms
Graph Flow Algorithms: Page Rank
- Week 6:
Prediction Evaluation Methods
Interface Alternatives and Extensions
- Week 7:
Anonymity and Privacy
Deliberate Manipulation and Defenses
Course Work and Assessment
- 4 Assignments 30%
Assignments will include exercise problems on the economic models studied, and short-answer questions on the papers discussed in class.
- Participation in the class, and on the CTools discussion board 10%
- Final Paper 60%
The final paper will involve designing a recommender system for a
particular domain. It will consider a potential application setting, explorte the entire design space covered in the course, and consider each of the known pitfalls. It will culminate in a set of design recommendations.
In Fall 2006, this course was taught by Prof. Paul Resnick. The previous course
website can be found here.