SI 583 Recommender Systems
Instructor: Rahul Sami
1.5 Credit, 7-week course module
First half of Winter 2012
Tuesday, Thursday 8:30am-10:00am, 2245NQ
Office hours: Monday 4-5pm, Tuesday 3-4pm (4340NQ)
Course Learning Objectives:
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.
Academic Integrity Policy
The UM and SI Academic Integrity Policy applies to this course:
Collaboration while working on homework
problems, and while discussing and interpreting the reading assignments, is
learning is effective. Collaboration will be especially valuable in summarizing the
reading materials and picking out the key concepts. You must, however, write your
homework submission on your own, in your own words, before turning it in. If you
worked with someone on the homework before writing it, you must list any and all
collaborators on your written submission.
All written submissions must be your own, original work. Original
work for narrative questions is not mere paraphrasing of someone else's completed
answer: you must not share written answers with each other at all. At most, you
should be working from notes you took while participating in a study session. Largely duplicate copies of the same assignment will receive an equal division of the total
point score from the one piece of work.
You may incorporate selected excerpts from publications by other authors, but
they must be clearly marked as quotations and must be attributed. If you build
on the ideas of prior authors, you must cite their work. You may obtain copy
editing assistance, and you may discuss your ideas with others, but all substantive writing and ideas must be your own, or be explicitly attributed to another.
Rackham Graduate policy on Academic and Professional Integrity
for the definition of plagiarism, and associated consequences.
Accommodations for Students with Disabilities
If you think you need an accommodation for a disability, please let me
know at your earliest convenience. Some aspects of this course, the assignments, the in-class activities, and the way we teach may be modified
to facilitate your participation and progress. As soon as you make me
aware of your needs, we can work with the Office of Services for Students
with Disabilities (SSD) to help us determine appropriate accommodations. SSD (734-763-3000; http://www.umich.edu/sswd) typically recommends accommodations through a Verified Individualized Services and
Accommodations (VISA) form. I will treat any information you provide
as private and confidential.
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.
- 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
- Week 0:
Introduction to the recommender systems design space
- Week 1:
Eliciting Ratings and other Feedback Contributions
- Week 2:
Linear Algebra notation;
User-User Recommender Algorithm
- Week 3:
Item-Item Recommender Algorithm
Singular Value Decomposition for Collaborative Filtering
- Week 4:
Content Filtering and Hybrid Methods
Evaluation of Recommenders
- Week 5:
Applications, and Case Study: Recommending Messages in an Online Community
Recommenders and Social Networks
- Week 6:
Explanations; other Interface Extensions
- Week 7:
Deliberate Manipulation and Defenses; Privacy
Course Work and Assessment
- 4 Assignments 30%
Assignments will include exercise problems on the recommendation models studied, and short-answer questions on the papers and topics discussed in class.
- Participation in the class, and on the CTools discussion forum 10%
- Final Paper 60%
The final paper (6-8 pages long) will involve designing a recommender system for a
particular domain. It will consider a potential application setting, explore the entire design space covered in the course, and consider each of the known pitfalls. It will culminate in a set of design recommendations.