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: T, Th 11am-12:15pm, Manning 307 (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%.
Schedule: Subject to change! The required textbook (Croft, Metzler, and Strohman) is denoted as CMS below.
Lecture Date Events Topic Reading Due
1 Thu. 1/9   Introduction to Information Retrieval: The Big Picture  
2 Tue. 1/14   Course Overview: Roadmap and Expectations CMS Ch. 1
3 Thu. 1/16   Introduction To Ad-hoc Retrieval I CMS Ch. 2, CMS 7.0-7.1
4 Tue. 1/21   Introduction To Ad-hoc Retrieval II  
5 Thu. 1/23 HW1 Out Indexing and Query Processing CMS Ch. 5.0-5.3
6 Tue. 1/28   Statistical Properties of Text I CMS Ch. 4.0-4.2
7 Thu. 1/30   Statistical Properties of Text II  
8 Tue. 2/4   Text Representation CMS Ch. 4.3-4.7, MRS Ch. 2
9 Thu. 2/6 HW1 Due Vector Space Model I CMS Ch. 7.0-7.1.2
10 Tue. 2/11   Vector Space Model II  
11 Thu. 2/13   Query Likelihood Model I CMS Ch. 7.3, CMS 4.5
12 Tue. 2/18 HW2 Out Query Likelihood Model I  
13 Thu. 2/20 Literature Review Proposal Due Document Priors  
14 Tue. 2/25   Midterm Review  
15 Thu. 2/27   Midterm  
16 Tue. 3/4 HW2 Due Evaluation Overview CMS Ch. 8
17 Thu. 3/6   Test Collection Evaluation I Sanderson '10 (pages 248-298), Hersh et al., '00, Turpin & Hersh '01
18 Tue. 3/11 Spring Break (No Class)    
19 Thu. 3/13 Spring Break (No Class)    
20 Tue. 3/18   Test Collection Evaluation II Sanderson '10 (pages 308-350)
21 Thu. 3/20   Experimentation I Smucker et al., '07, Cross-Validation, Parameter Tunning and Overfitting
22 Tue. 3/25 HW3 Out Experimentation II  
23 Thu. 3/27   Interactive Information Retrieval I Arguello & Crescenzi '19
24 Tue. 4/1   Interactive Information Retrieval II  
25 Thu. 4/3   Search Log Analysis Joachims et al., '05
26 Tue. 4/8 HW3 Due A/B Testinig I Dmitriev et al., '17, Video Tutorial (Kohavi et al. '17)
27 Thu. 4/10   A/B Testing II  
28 Tue. 4/15   Literature Review Presentations I  
29 Thu. 4/17 Well-being Day (No Class)    
30 Tue. 4/22   Literature Review Presentations II  
31 Thu. 4/24 Literature Review Due Literature Review Presentations III  
32 Mon. 5/5 Final Exam