Search Engines:
11-442 / 11-642
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(For Spring 2020, click here)

Course Description: This lecture-oriented course studies the theory, design, and implementation of text-based search engines. The core components include statistical characteristics of text, representation of information needs and documents, several important retrieval models, and experimental evaluation. The course also covers common elements of commercial search engines, for example, integration of diverse search engines into a single search service (federated search, vertical search), 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 worth 12 units. The undergraduate section (11-442) is worth 9 units.

The main difference between 11-442 and 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 architectures, methods of representing text documents, methods of representing information needs, and methods of evaluating search effectiveness;
  • Implement well-known retrieval 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 midterm and final 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 Java programming language, thus students must also have good Java programming skills. Assignments will be done using Java 11.
Time & Location: Tu/Th 10:30-11:50, BH A51
Instructor: Jamie Callan
Teaching Assistants:
He Phoebe Cui (hecui@andrew)
Shubham Mehrotra (shubhammehrotra@cmu)
Ramith Padaki (rpadaki@andrew)
Anqi Wang (anqiw2@andrew)
Xue Xia (xuex@cs)
Jiahui Alice Zheng (jiahuiz@andrew)
Office hours:
Monday 2:00-3:30
GHC 6404
GHC 5417
Tuesday 12:00-1:30
GHC 5417
GHC 5417
Wednesday 1:00-2:30 Anqi GHC 5417
Friday 4:00-5:30 Shubham GHC 5417
Course Materials: 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.

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

Online access to some materials (additional readings, lecture notes, datasets, etc) is restricted to the domain. CMU people can get access from outside (e.g., from home) using CMU's virtual private networking (vpn) service.

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 will periodically remove students that do not have CMU email addresses.
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 (10% each, 50% total), midterm exam (25%), final exam (25%).
Grading Scale: Grades are assigned using a curve. Typically the median GPA is about 3.5.
Course policies: Academic Integrity, Attendance, Auditing, Laptops & mobile devices, Late homework, Pass/Fail, Recording & videotaping, Waitlist
Date Topic Readings
Aug 27, Course overview (pdf, mp4)
Aug 29, Introduction to search: Exact-match retrieval (pdf, mp4) Ch 1, Ch 5.1.1 - 5.1.2
Sep 3, Introduction to search: Query processing (pdf, mp4)
Software development requirements (pdf, mp4)
HW1 out
Ch 2.4.2
Sep 5, Introduction to search: QryEval (pdf, mp4)
Optional HW1 recitation session (pdf)
6:00-7:00 pm, 1212 Doherty Hall
Ch 8-8.4 (early reading)
Sep 10, Evaluating search effectiveness (pdf, mp4 I, mp4 II) Ch 8.5
Sep 12, Best-match retrieval: VSM, BM25 (pdf, mp4) Ch 6.2-6.4.2, 11.4.3
Sep 17, Best-match retrieval: Language models (pdf, mp4)
HW1 due, HW2 out
Ch 12.2-12.4
Sep 19, HW2 implementation (pdf, mp4)
Document representation (pdf, mp4)
Sep 24, Query structure: Information needs and queries (pdf, mp4) Nguyen & Callan, 2011
Sep 26, Document representation (pdf, mp4) Ch 2.2
Oct 1, Query structure: Relevance and pseudo relevance feedback (pdf, mp4)
HW2 due, HW3 out
Ch 9-9.2.2
Oct 3, Index creation (pdf, mp4) Ch 4
Oct 8, Class cancelled (start HW3!) Ch 5.3-5.3.1, Ch 7.1.3
Oct 10, Large-scale indexes (pdf, mp4)
Oct 15, Document structure (pdf, mp4) Ch 10-10.3
Oct 17, Midterm Exam Sample Midterm 1,
Oct 22, Ranked retrieval: Feature-based models
HW3 due, HW4 out
Li, 2011
Oct 24, Authority metrics Ch 21
Oct 29, Ranked retrieval: Neural models  
Oct 31, Ranked retrieval: Neural models Guo, et al, 2016  
Nov 5, Diversity
HW4 due, HW5 out
Carbonell & Goldstein, 1998
Nov 7, Diversity Santos, et al., 2010, Dang & Croft, 2012
Nov 12, Evaluating search effectiveness  
Nov 14, Search log analysis Eickhoff et al, 2014
Nov 19, Search log analysis
HW5 due
Bennett et al, 2012 (early reading)
Nov 21, Personalization  
Nov 26, Federated, aggregated, & vertical search Arguello & Diaz, 2013, Ch 1 - 1.3.1
Dec 3, Enterprise search  
Dec 5, 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
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 Consider reaching out to a friend, faculty or family member you trust for help getting connected to the support that can help.

Copyright 2019, Carnegie Mellon University.
Updated on October 21, 2019

Jamie Callan