INLS 509:
Information Retrieval

Description: The field of information retrieval (IR) is concerned with the analysis, organization, storage, and retrieval of unstructured and semi-structured data. In this course, we will focus on mostly text. While IR systems are often associated with Web search engines (e.g., Google), IR applications also include digital library search, patent search, search for local businesses, and expert search, to name a few. Likewise, IR techniques (the underlying technology behind IR systems) are used to solve a wide range of problems, such as organizing documents into an ontology, recommending news stories to users, detecting spam, and predicting reading difficulty. This course will provide an overview of the theory, implementation, and evaluation of IR systems and IR techniques. In particular, we will explore how search engines work, how they "interpret" human language, what different users expect from them, how they are evaluated, why they sometimes fail, and how they might be improved in the future.
Prerequisites: There are no prerequisites for this course.
Expectations: Information retrieval is the study of computer-based solutions to a human problem. Thus, the first half of the course will be system-focused, while the second half will be user-focused. During the first half, you should expect to see some math (e.g., basic probability and statistics and some linear algebra). However, we will focus on the concepts rather than the details.

Students will have an opportunity to explore their interests with a open-ended literature review.

Time & Location: M,W 11:15am-12:30pm, Manning 001 (in-person)
Instructor: Jaime Arguello (email, web)
Office Hours: By appointment, Manning 10 (Garden Level)
Required Textbook: Search Engines - Information Retrieval in Practice, W. B. Croft, D. Metzler, and T. Strohman. Cambridge University Press. 2009. Available on-line.
Additional Resources: Foundations of Statistical Natural Language Processing. C. Manning and H Schutze. 1999.

Introduction to Information Retrieval. C. Manning, P. Raghavan and H. Schutze. 2008.
Other Readings: Selected papers and chapters from other books will sometimes be assigned for reading. These will be available online.
Course Policies: Laptops, Attendance, Participation, Collaboration, Plagiarism & Cheating, Late Policy, Use of Generative AI Tools
Grading: 30% homework (10% each)
15% midterm exam
15% final exam
30% literature review (5% proposal, 10% presentation, 15% paper)
10% participation
Grade Assignments: Undergraduate grading scale: A+ 97-100%, A 94-96%, A- 90-93%, B+ 87-89%, B 84-86, B- 80-83%, C+ 77-79%, C 74-76%, C- 70-73%, D+ 67-69%, D 64-66%, D- 60-63%, F 0-59%

Graduate grading scale: H 95-100%, P 80-94%, L 60-79%, and F 0-59%.

All assignments, exams, and the literature review will be graded on a curve.
Schedule: Subject to change! The required textbook (Croft, Metzler, and Strohman) is denoted as CMS below.
Lecture Date Events Topic Reading Due
1Wed. 1/10Class Cancelled  
2Mon. 1/15MLK Day (No Class)  
3Wed. 1/17 Introduction to Information Retrieval: The Big Picture 
4Mon. 1/22 Course Overview: Roadmap and ExpectationsCMS Ch. 1
5Wed. 1/24 Introduction To Ad-hoc Retrieval ICMS Ch. 2, CMS 7.0-7.1
6Mon. 1/29HW1 OutIntroduction To Ad-hoc Retrieval II 
7Wed. 1/31 Indexing and Query ProcessingCMS Ch. 5.0-5.3
8Mon. 2/5 Statistical Properties of Text ICMS Ch. 4.0-4.2
9Wed. 2/7 Statistical Properties of Text II 
10Mon. 2/12Well-Being Day (No Class)  
11Wed. 2/14HW1 Due, HW2 OutText RepresentationCMS Ch. 4.3-4.7, MRS Ch. 2
12Mon. 2/19 Vector Space Model ICMS Ch. 7.0-7.1.2
13Wed. 2/21Literature Review Proposal DueVector Space Model II 
14Mon. 2/26 Query Likelihood Model ICMS Ch. 7.3, CMS 4.5
15Wed. 2/28HW2 DueQuery Likelihood Model I
16Mon. 3/4Midterm ReviewMidterm Review 
21Wed. 3/6MidtermMidterm 
22Mon. 3/11Spring Break (No Class)  
23Wed. 3/13Spring Break (No Class)  
24Mon. 3/18 Document Priors 
25Wed. 3/20 Evaluation OverviewCMS Ch. 8
26Mon. 3/25HW3 OutTest Collection Evaluation ISanderson '10 (pages 248-298), Hersh et al., '00, Turpin & Hersh '01
27Wed. 3/27 Test Collection Evaluation IISanderson '10 (pages 308-350)
28Mon. 4/1 Experimentation ISmucker et al., '07, Cross-Validation, Parameter Tunning and Overfitting
29Wed. 4/3 Experimentation II 
30Mon. 4/8HW3 DueInteractive Information Retrieval IArguello & Crescenzi '19
31Wed. 4/10 Interactive Information Retrieval II 
32Mon. 4/15 Search Log AnalysisJoachims et al., '05
33Wed. 4/17 A/B Testing IDmitriev et al., '17, Video Tutorial (Kohavi et al. '17)
34Mon. 4/22 A/B Testing II 
35Wed. 4/24 Literature Review Presentations I 
36Mon. 4/29 Literature Review Presentations II 
37Thu. 5/2Final Exam Due 
38Fri. 5/3Literature Review Due