Search Engines:
11-442 / 11-642
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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, diverse search results, and sponsored search. 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, Statistics & Data Science Methods (helpful)
Time & Location: Tu/Th 10:30-11:50, BH A51
Instructor: Jamie Callan
Teaching Assistants:
Anusha Prakash (anushap@andrew) Reading summaries
Anuva Agarwal (anuvaa@andrew)
Ganesh Palanikumar(gpalanik@andrew)
Pravalika Avvaru(pavvaru@andrew) Software testing
Raghuveer Chanda(rchanda@andrew)
Sheng Sun(shengs1@andrew)
Shravya Kaudki Srinivas(skaudkis@andrew) Reading summaries
Srividya Potharaju (spothara@andrew)
Office hours:
Day Time Location TA
Monday 12:30-2:00
GHC 5417
GHC 5417
Tuesday 1:00-2:30 GHC 5417 Srividya
Thursday3:00-4:30 GHC 5417 Raghu
Friday 4:00-5:30 GHC 5417 Sheng
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 WebVPN 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: Weekly reading summaries (10% total), 5 homework assignments (10% each, 50% 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, Laptops & mobile devices, Late homework, Pass/Fail, Recording & videotaping, Waitlist
Date Topic Readings
Aug 28, Course overview (pdf, mp4)
Aug 30, Introduction to search: Exact-match retrieval (pdf, mp4)
Reading summaries (pdf, mp4)
Ch 1, Ch 5.1
Sep 4, Introduction to search: Query processing (pdf, mp4)
HW1 out
Ch 2.4
Sep 6, Introduction to search: QryEval (pdf, mp4)
Software development requirements (pdf, mp4)
Sep 10, Optional HW1 recitation sessions
6:30-8:00 pm
Two locations: NSH 3002 and GHC 4401
Sep 11, Evaluating search effectiveness (pdf, mp4 #1, mp4 #2) Ch 8-8.5
Sep 13, Evaluating search effectiveness (pdf, mp4)  
Sep 18, Best-match retrieval: VSM, BM25 (pdf, mp4)
HW1 due, HW2 out
Ch 6.0, 6.2-6.4.2, 11.4
Sep 20, Best-match retrieval: Language models (pdf, mp4)
HW2 implementation notes (pdf)
Ch 12
Sep 25, Query structure: Information needs and queries Nguyen & Callan, 2011
Sep 27, Document representation Ch 2-2.2
Oct 2, Query structure: Relevance and pseudo relevance feedback
HW2 due, HW3 out
Ch 9
Oct 4, Index creation Ch 4
Oct 9, Index creation Ch 7
Oct 11, Large-scale indexes
Oct 16, Document structure Ch 10
Oct 18, Midterm Exam Sample Midterm 1, Sample Midterm 2
Oct 23, Ranked retrieval: Feature-based models
HW3 due, HW4 out
Clarke Ch 11.7; Li, 2011
Oct 25, Authority metrics Ch 21
Oct 30, Ranked retrieval: Neural models  
Nov 1, Ranked retrieval: Neural models Guo, et al, 2016  
Nov 6, Diversity
HW4 due, HW5 out
Santos, Ch 1-3
Nov 8, Diversity Santos, Ch 4-5
Nov 13, TBD  
Nov 15, Search log analysis Eickhoff et al, 2014
Nov 20, Search log analysis
HW5 due
Nov 27, Personalization Bennett et al, 2012
Nov 29, Federated, aggregated, & vertical search Si & Callan, 2003, Arguello & Diaz, 2013
Dec 4, Enterprise search  
Dec 6, 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 2018, Carnegie Mellon University.
Updated on September 20, 2018

Jamie Callan