Search Engines:
11-442 / 11-642 / 11-742
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Spring 2020
(click here for Fall 2020)

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 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 &
Tu/Th 1:30-2:50, Location DH A302
Instructor: Jamie Callan
Teaching Assistants:
Anqi Wang (anqiw2@andrew)
Linwei Henry Li (linweil@andrew)
Vinay Damodaran (vdamodar@andrew)
Sharanya Chakravarthy (sharanyc@andrew)
Xinyi Tao (xinyit@andrew)
Ziqi Deng (ziqideng@andrew)
Office hours:
Monday 1:00-2:30
Tuesday 4:00-5:30 Linwei Henry
Wednesday 12:00-1:30 Ziqi
Thursday 4:30-6:00 Sharanya
Friday 6:30-8:00 Vinay
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.
5 homework assignments (10% each, 50% total), midterm exam (25%), final exam (25%).
5 homework assignments (12.5% each, 62.5% total), midterm exam (37.5%).
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, Recording & videotaping, Waitlist
Date Topic Readings
Jan 14, Course overview (pdf, mp4)
Jan 16, Introduction to search: Exact-match retrieval (pdf, mp4) Ch 1, Ch 5.1.1 - 5.1.2
Jan 21, Introduction to search: Query processing (pdf, mp4)
Software development requirements (pdf)
HW1 out
Ch 2.4.2
Jan 23, Introduction to search: QryEval (pdf, mp4)
5:00-6:30pm, Optional HW1 recitation, DH 2315 (pdf)
Ch 8-8.4 (early reading)
Jan 28, Evaluating search effectiveness (pdf, mp4) Ch 8.5
Jan 30, Best-match retrieval: VSM, BM25 (pdf, mp4) Ch 6.2-6.4.2, 11.4.3
Feb 4, Best-match retrieval: Language models (pdf, mp4)
HW1 due, HW2 out
Ch 12.2-12.4
Feb 6, HW2 implementation (pdf, mp4)
Document representation (pdf, mp4)
Document priors (pdf, mp4)
Feb 11, Query structure: Information needs and queries (pdf, mp4) Nguyen & Callan, 2011
Feb 13, Document representation (pdf, mp4) Ch 2.2
Feb 18, Query structure: Relevance and pseudo relevance feedback (pdf, mp4)
HW2 due, HW3 out
Ch 9-9.2.2
Feb 20, Index creation (pdf, mp4) Ch 4
Feb 25, Large-scale indexes (pdf, mp4) Ch 5.3-5.3.1, Ch 7.1.3
Feb 27, Document structure (pdf, mp4)
Mar 3, Document structure (pdf, mp4)
Ranked retrieval: Feature-based models (pdf, mp4)
Ch 10-10.3
Mar 5, Midterm Exam Sample Midterm 1,
Mar 17, Class cancelled  
Mar 19, Ranked retrieval: Feature-based models (pdf, mp4) Li, 2011
Mar 24, Authority metrics (pdf, mp4)
Ranked retrieval: Neural models (pdf, mp4)
HW3 due, HW4 out
Ch 21
Mar 26, Ranked retrieval: Neural models (pdf, mp4) Guo, et al, 2016
Mar 31, Diversity (pdf, mp4) Carbonell & Goldstein, 1998
Apr 2, Diversity (pdf, mp4) Santos, et al., 2010, Dang & Croft, 2012
Apr 7, Evaluating search effectiveness (pdf, mp4)
HW4 due, HW5 out
Apr 9, Search log analysis (pdf, mp4) Eickhoff et al, 2014
Apr 14, Search log analysis (pdf, mp4) Bennett et al, 2012
Apr 16, Personalization (pdf, mp4)  
Apr 21, Federated, aggregated, & vertical search (pdf, mp4)
HW5 due
Arguello & Diaz, 2013, Ch 1 - 1.3.1
Apr 23, Enterprise search (pdf, mp4)  
Apr 28, Recent research: Better representations for search (pdf, mp4) Devlin, et al., 2019, Dai and Callan, 2019
Apr 30,
Final exam
No class
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 2020, Carnegie Mellon University.
Updated on April 28, 2020

Jamie Callan