CSE 5/635: Information Retrieval:

Fig. 1: Vannevar Bush's Memex, a hypothetical electro-mechanical hypertext system described in 1945 and arguably the blueprint for modern information systems.

How many information retrieval systems have you used since waking up this morning? Probably more than you think. Information retrieval systems, including but not limited to web search engines, product recommender systems, library catalogues, and social media applications represent vital tools for navigating our modern information ecosystem. The underlying algorithms and technologies that power these systems come from every corner of computer and information science, and have a rich and fascinating history.

In this course, we will study the art and science of information retrieval. We will cover a wide range of technical topics and applications of IR. Furthermore, because information is generally produced and consumed by humans, we will have a particular focus on issues surrounding human users of IR systems.

The course will not involve a final exam; however, it will involve a final project, which will include an in-class presentation as well as a formal written paper. There will also be several homework assignments, including a pilot study for the final project. Furthermore, students will be expected to present at least one paper in class as well as participate in class discussions. There will also be a considerable amount of reading assigned, which students will be expected to actually do.

Monday & Wednesday, 16:00 – 17:30

Note: Topics and speakers may change through the term as we adjust our course plan.

Week Day Date Topic Materials Discussant HW
1 Mon 4/1/2019 Course Overview, Information-Seeking Behavior lecture_1.1a.pdf
1 Wed 4/3/2019 No class - SDB giving workshop lecture_1.2.pdf
2 Mon 4/8/2019 IR Basics lecture_2.1.pdf
2 Wed 4/10/2019 IR Models: Boolean, Vector, Probablistic lecture_2.2.pdf HW2
3 Mon 4/15/2019 Index Construction/Optimization/Compression lecture_3.1.pdf
3 Wed 4/17/2019 Experimental Evaluation
4 Mon 4/22/2019 Bayesian & Model-based approaches to evaluation lecture_4.1.pdf
4 Wed 4/24/2019 Web Search, PageRank
5 Mon 4/29/2019 Search UI/UX
5 Wed 5/1/2019 User Behavior
6 Mon 5/6/2019 Pilot Study Presentations
6 Wed 5/8/2019 Machine Learning & Ranking HW3
7 Mon 5/13/2019 Relevance Feedback
7 Wed 5/15/2019 Query Suggestion/Reformulation BiomedicalIR.pdf
8 Mon 5/20/2019 Multimedia Retrieval
8 Wed 5/22/2019 Document Clustering
9 Mon 5/27/2019 No class - SDB Traveling
9 Wed 5/29/2019 No class - SDB Traveling
10 Mon 6/3/2019 Microblog search
10 Wed 6/5/2019 Cross-Language IR
11 Mon 6/10/2019 Personalization: Time & Space
11 Wed 6/13/2019 Ethical Considerations in IR
12 Mon 6/17/2019 Project Presentations
12 Wed 6/19/2019 Project Presentations

Assigned readings for each session may be found below, grouped by week. Students are expected to have read each week's readings before class (excepting the first session), and to be prepared to discuss them in class. Most readings are available through the OHSU Library; we will provide copies of other articles as needed.

Additionally, students will be expected to take turns leading discussions (to be counted towards the participation component of the grade). The precise logistics of this will be determined once we know about the final enrollment.

There will be several hands-on homework assignments given throughout the class. Each assignment will come with a due date, which should be treated as firm. If you need an extension, please make your request by 5:00 PM two days prior to the assignment's due date. In other words, if an assignment is due on a Tuesday, extension requests must be made by Sunday afternoon. This is to help prevent procrastination, to give you time to come to me for help, and to help me plan my grading time.

In addition to homework assignments, there will be a final project on the subject of your choice. In the past, students have replicated a paper, implemented an algorithm or approach, added a feature to an existing IR system, participated in a shared task (TREC, CLEF, etc.), and so on. The deliverables for the project are:

  1. A pilot study, and accompanying in-class presentation mid-way through the term;
  2. an in-class presentation at the end of the term of your final results;
  3. a conference-style writeup (8-10 pages not counting references).

Grading will be as follows:

  • 30% Participation (including leading discussion)
  • 30% Homework
  • 40% Final project (including pilot study)

  • Manning C, Raghavan P, and Schütze H. Introduction to Information Retrieval. Cambridge University Press, 2008. Available Online
  • Hearst M. Search User Interfaces. Cambridge University Press, 2009. Available Online

Useful Books

Assigned Readings

Week 1, Monday: Course Overview, Information-Seeking Behavior

Week 1, Wednesday: No class - SDB giving workshop

Week 2, Monday: IR Basics

Week 2, Wednesday: IR Models: Boolean, Vector, Probablistic

Week 3, Monday: Index Construction/Optimization/Compression

Week 3, Wednesday: Experimental Evaluation

Week 4, Monday: Bayesian & Model-based approaches to evaluation

Week 4, Wednesday: Web Search, PageRank

Week 5, Monday: Search UI/UX

Week 5, Wednesday: User Behavior

Week 6, Monday: Pilot Study Presentations

Week 6, Wednesday: Machine Learning & Ranking

Week 7, Monday: Relevance Feedback

Week 7, Wednesday: Query Suggestion/Reformulation

Week 8, Monday: Multimedia Retrieval

Week 8, Wednesday: Document Clustering

Week 9, Monday: No class - SDB Traveling

Week 9, Wednesday: No class - SDB Traveling

Week 10, Monday: Microblog search

Week 10, Wednesday: Cross-Language IR

Week 11, Monday: Personalization: Time & Space

Week 11, Thursday: Ethical Considerations in IR

Week 12, Monday: Project Presentations

Week 12, Wednesday: Project Presentations

Steven Bedrick


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