Search Engines:
11-442 / 11-642 / 11-742
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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 sections (11-642 and 11-742) are worth 12 units. The undergraduate section (11-442) is worth 9 units.

The main difference between the three sections (11-442, 11-642, 11-742) 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 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 &
Tu/Th 10:10 AM - 11:30 AM (ET), Posner Hall 153 (POS 153) and Zoom.
Zoom invitation (requires VPN or login to read), Zoom instructions for this class, general Zoom instructions.
Instructor: Jamie Callan
Teaching Assistants:
HongChien Yu(hongqiay@andrew)
Luyu Gao(luyug@andrew)
Swetha Gangu(sgangu@andrew)
Zhen Fan(zhenfan@andrew)
Office hours:
Day Time LocationTA
Monday 1:30-3:00GHC 5417HongChien
Tuesday 4:45-6:15GHC 5417Ran
Wednesday2:00-3:30GHC 5417Zhen
Thursday 2:00-3:30GHC 5417Luyu
Friday 4:30-6:00GHC 5417Swetha
Lecture Slides: Copies of the lecture slides are posted on this page, usually within 24 hours.

Lecture Recordings: Lecture recordings are posted on this page, usually within 24 hours. Recordings are access-controlled. Use your Andrew ID and password to login to YouTube. Lecture recordings may unintentionally include your face or voice. Please alert the instructor if this is a problem for you.

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 will periodically remove students that do not have CMU email addresses.

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%).
Grades are assigned using a curve. Typically the median GPA is about 3.5.
Academic Integrity, Attendance, Auditing, Laptops & mobile devices, Late homework, Pass/Fail, Waitlist
Classroom Expectations During a Pandemic:
Carnegie Mellon wants to offer you an in-person university experience this year, but we are still in the middle of a pandemic. This will only work if we all cooperate to keep COVID-19 from spreading in our community. Students are expected to wear a facial covering ("mask") while in the classroom. Eating and drinking are not permitted in this class. If you do not wear a facial covering, I will ask you to put one on or leave the room. If you do not comply, I will cancel the lecture and refer you to the Office of Community Standards and Integrity. If you are uncomfortable with this policy, please consider taking the course in a future semester after the threat from COVID-19 has subsided.
Date Topic Readings
Aug 31, Course overview (pdf, mp4)
Sep 2, Introduction to search: Exact-match retrieval (pdf, mp4) Ch 1, Ch 5.1.1 - 5.1.2
Sep 7, Introduction to search: Query processing (pdf, mp4)
Software development requirements (pdf)
HW1 out
Ch 2.4.2
Sep 9, Introduction to search: QryEval (pdf, mp4) Ch 8-8.4 (early reading)
Sep 10, HW1 Recitation (optional), 4:00-5:30pm, POS 153 (pdf, mp4)  
Sep 14, Evaluating search effectiveness (pdf, mp4) Ch 8.5
Sep 16, Best-match retrieval: VSM, BM25 (pdf, mp4) Ch 6.2-6.4.2, 11.4.3
Sep 21, Best-match retrieval: Language models (pdf, mp4)
HW2 implementation (pdf)
HW1 due, HW2 out
Ch 12.2-12.4
Sep 23, Query structure: Information needs and queries (pdf, mp4)
Document representation (pdf, mp4)
Nguyen & Callan, 2011
Sep 28, Document representation (pdf, mp4) Ch 4, Ch 5.3-5.3.1, Ch 7.1.3
Sep 30, Query structure: Relevance and pseudo relevance feedback (pdf, mp4) Ch 2.2
Oct 5, Index creation (pdf, mp4)
HW2 due, HW3 out
Ch 9-9.2.2
Oct 7, Large-scale indexing (pdf, mp4)  
Oct 12, Midterm exam Sample Midterm
Oct 14, Mid-semester break
Oct 19, Ranked retrieval: Feature-based models (pdf, mp4)
HW3 due, HW4 out
Li, 2011
Oct 21, Ranked retrieval: Feature-based models
Authority metrics
Ch 21
Oct 26, Diversity Santos, et al., 2010, Dang & Croft, 2012
Oct 28, Diversity  
Nov 2, TBD
HW4 due, HW5 out
Nov 4, Document structure Ch 10-10.3
Nov 9, Ranked retrieval: Neural models  
Nov 11, Ranked retrieval: Neural models Guo, et al, 2016
Nov 16, Ranked retrieval: Neural models
HW5 due
Dai & Callan, 2019a, Dai & Callan, 2019b
Nov 18, Search log analysis  
Nov 23, Search log analysis Eickhoff et al, 2014
Nov 25, Thanksgiving break
Nov 30, Personalization Bennett et al, 2012
Dec 2, Final exam  
Accommodations for COVID-19: If you are unable to attend class: See the Time & Location and Course Materials section above for details on watching lectures live via Zoom or recordings of lectures via YouTube.

If the instructor is unable to attend class: Students will be notified via email from Canvas that the lecture will be delivered via Zoom or replaced by a recorded lecture from last semester.
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
If You Are Having
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 Consider reaching out to a friend, faculty or family member you trust for help getting connected to the support that can help.

Copyright 2021, Carnegie Mellon University.
Updated on October 20, 2021

Jamie Callan