Search Engines:
11-442 / 11-642
 
CMU logo
 

Spring 2025
(Fall 2025 is here)

Course
Description: 		This lecture-oriented course studies the theory, design, and implementation of text-based search engines, retrieval augmented generation, and recommender systems. The core components include statistical characteristics of text, several important lexical retrieval models, several recent neural models, experimental evaluation, and fair ranking. The course also covers common elements of commercial search engines, for example, personalized search results and diverse search results. The software architecture components include design and implementation of large-scale, distributed search engines.

This is a full-semester course. The graduate section (11-642) is 12 units. The undergraduate section (11-442) is 9 units.

The main difference between the two sections (11-442, 11-642) is the amount of analysis, writing, and time required to complete homework assignments.
Learning Objectives: By the end of the course, students are expected to have developed the skills listed below.
  • Recall and discuss well-known search engine and recommender system architectures, methods of representing text documents, methods of representing information needs, and methods of evaluating search effectiveness;
  • Implement well-known retrieval and recommendation algorithms, and test them on standard datasets; and
  • Apply information retrieval techniques discussed in class to solve problems faced by governments and companies.

Skills are assessed by the homework assignments; and by exams.

Eligibility: This course is open to all students who meet the prerequisites.
Prerequisites: This course requires good programming skills and an understanding of computer architectures and operating systems (e.g., memory vs. disk trade-offs). A basic understanding of probability, statistics, and linear algebra is helpful. Thus students should have preparation comparable to the following CMU undergraduate courses.
  • 15-210, Parallel and Sequential Data Structures and Algorithms (required)
  • 15-213, Introduction to Computer Systems (required)
  • 15-451, Algorithm Design and Analysis (helpful)
  • 21-241, Matrices and Linear Transformations or 21-341, Linear Algebra (required)
  • 21-325, Probability (required)
  • 36-202, Methods for Statistics & Data Science (helpful)
Homework assignments are done in the Python programming language, thus students must also have good Python programming skills.
Time &
Location: 		Tu/Th 3:30-4:50, POS 151
Instructors: Jamie Callan and Fernando Diaz
Teaching Assistants:
Rebecca Byrnes rbyrnes@andrew
Angela Chenangelac3@andrew
Prabhleen Lamba plamba@andrew
Aditi Saini aditisai@andrew
Office hours:
Monday
 		 12:00-1:30
  4:00-5:30		 Prabhleen
Rebecca		 GCH 5417
WeH 3002
Tuesday 12:00-1:30 Angela GHC 5417
Friday 10:30-12:00 Aditi GHC 5417
Course
Materials: 		Lecture Slides: Copies of the lecture slides are posted on this page, usually within 24 hours.

Textbook: The textbook is Introduction to Information Retrieval, Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schutze, Cambridge University Press. 2008. You may use the printed copy or the online copy, but note that the reading instructions refer to the printed copy.

Readings: There are additional selected readings, which will be available through the class web page (this page).

Piazza: A discussion forum is provided for students to ask questions, answer questions, and discuss class-related topics. You must register yourself to access the discussion forum. Please provide a CMU email address when you join the discussion (you can use other email addresses, too). We may periodically remove people that do not have CMU email addresses.

Homework Services: A Homework Services web page provides information about your homework submissions and access to graded homework reports. Each individual homework has its own web pages that describe the assignment and provide access to automated testing services.

Restricted access: Online access to some materials (additional readings, lecture notes, datasets, etc) is restricted to CMU people. Students on CMU local and virtual private networking IP addresses have direct access. Other students can gain access using a password.
Homework: 5 assignments that give hands-on experience with techniques discussed in class. Homework must be done individually, and students may not share their work with other students. See the course Academic Integrity policy for more information.
Grading: 5 homework assignments (12% each, 60% total), midterm exam (20%), final exam (20%).
Grading
Scale: 		Grades are assigned using a curve. Typically the median GPA is about 3.5.
Course
policies: 		Academic Integrity, Attendance, Auditing, Generative AI, Laptops & mobile devices, Late homework, Pass/Fail, Waitlist
Syllabus:
Date Instructor Topic Slides Readings
Jan 14 JC Introduction to search: Exact-match retrieval pdf Ch 5.1
Jan 16 JC Introduction to search: DAAT, BM25, query operators pdf Ch 1, Ch 11.4.3
Jan 20 HW1 out
(Academic Integrity)
Jan 21 JC Introduction to search: QryEval pdf Ch 2.4.2
Jan 22 HW1 support session (optional)
5:00-6:00, GHC 4303
(pdf)
Jan 23 FD Offline evaluation pdf Ch 8-8.5
Jan 28 JC Best-match retrieval: VSM, language models pdf Ch 6.2-6.4.2, Ch 12.2-12.4
Jan 30 FD Online evaluation pdf Kelly and Teevan, 2003, Jiang et al., 2014
Feb 3 HW1 due, HW2 out  
Feb 4 FD Feature-based ranking models pdf Li, 2011
Feb 6 FD Feature-based ranking models, Authority metrics pdf Ch 21 - 21.2
Feb 11, FD Recommender systems 1: Introduction and basic algorithms pdf Personalized Machine Learning (Chapter 4), Fairness in Information Access Systems (Chapter 2)
Feb 13 FD Recommender systems 2: Feature-based models pdf Personalized Machine Learning (Chapter 5), Deep neural networks for youtube recommendations
Feb 17 HW2 due, HW3 out  
Feb 18 JC Index creation pdf Ch 4-4.5, Ch 5.3-5.3.1, Ch 2.3, Ch 7.1.3
Feb 20 JC Large-scale indexes pdf
Feb 25 JC Personalization pdf Eickhoff et al, 2014, Bennett et al, 2012
Feb 27 Midterm exam Sample Midterm, additional questions
Mar 11 JC Neural ranking models: Introduction pdf  
Mar 13 JC Neural ranking models: BERT reranking pdf Dai & Callan, 2019a
Mar 17 HW3 due, HW4 out  
Mar 18 JC Neural ranking models: Sparse models pdf Dai & Callan, 2019b
Mar 20 JC Neural ranking models: Dense models pdf Karpukhin, et al., 2020
Mar 25 JC Neural ranking models: Large language models pdf  
Mar 27 JC Neural ranking models: Retrieval augmented generation,
Relevance and pseduo relevance feedback		 pdf,
pdf		 Gao, et al., 2023
Mar 31 HW4 due, HW5 out  
Apr 1 FD Diversification: measurement pdf  
Apr 8 FD Diversification: optimization pdf Dang & Croft, 2012, Santos, et al., 2010
Apr 10 FD Fairness: measurement pdf
Apr 14 HW5 due  
Apr 15 FD Fairness: optimization pdf  
Apr 17 FD Reinforcement learning for search and recommendation (1) pdf
Apr 22 FD Reinforcement learning for search and recommendation (2) pdf
Apr 24 Final exam Sample Final
Accommodations for Students with Disabilities: If you have a disability and are registered with the Office of Disability Resources, I encourage you to use their online system to notify me of your accommodations and discuss your needs with me as early in the semester as possible. I will work with you to ensure that accommodations are provided as appropriate. If you suspect that you may have a disability and would benefit from accommodations but are not yet registered with the Office of Disability Resources, I encourage you to contact them at access@andrew.cmu.edu.
If You Are Having
Difficulty: 		If you are having difficulty in any of your courses, please consider reaching out to the Student Academic Success Center (SASC). SASC provides the following services.
  • Individual and small group coaching on the development of successful learning habits such as time management, stress reduction, and other skills (Academic Coaching).
  • Consultation for multilingual and international students (Language and Cross-Cultural Support).
  • Individual consultations and workshops to support excellence in communication of written texts, oral presentations, and data visualization (Communication Support).
Advice From
The Faculty: 		This course is a lot of work. Take care of yourself. Do your best to maintain a healthy lifestyle this semester by eating well, exercising, avoiding drugs and alcohol, getting enough sleep and taking some time to relax. This will help you achieve your goals and cope with stress.

If you find yourself struggling with the material or workload, please ask for help. All of us benefit from support during times of struggle. You are not alone. There are many helpful resources available on campus and an important part of the college experience is learning how to ask for help. Asking for support sooner rather than later is often helpful.

If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support. Counseling and Psychological Services (CaPS) is here to help: call 412-268-2922 and visit their website at https://www.cmu.edu/counseling/. Consider reaching out to a friend, faculty or family member you trust for help getting connected to the support that can help.

Copyright 2025, Carnegie Mellon University.
Updated on April 22, 2025

Jamie Callan
and
Fernando Diaz