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 lecture-oriented course worth 12 units.
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, Matrix Algebra or 21-341, Linear Algebra (required)
  • 21-325, Probability (required)
  • 36-202, Basic statistics (helpful)
Time & Location: Tu/Th 10:30-11:50, WEH 7500
Instructor: Jamie Callan
Teaching Assistants:
Rishabh Arora (rishabha@andrew)
Haoming Chen (haomingc@andrew)
Zhuyun Dai (zhuyund@cs)
Akshat Gaur (agaur@andrew)
Qinyu Hu (qinyuh@andrew)
Qing Liu (qingl2@andrew)
Arpita Pyreddy (mpyreddy@andrew)
Varshini Ramaseshan(vramases@andrew)
Office hours:
Day Time Location TA
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
(subject to revision):
Date Topic Readings
Aug 29, Course overview
Aug 31, Introduction to search: Exact-match retrieval
Reading summaries
Ch 1, Ch 5.1
Sep 5, Introduction to search: Query processing
HW1 out
Ch 2.4
Sep 7, Introduction to search: Query processing
Software development guidelines
Sep 12, Evaluating search effectiveness Ch 8-8.5
Sep 14, Evaluating search effectiveness  
Sep 19, Document representation
HW1 due, HW2 out
Ch 2-2.2
Sep 21, Best-match retrieval: VSM, BM25 Ch 6, Ch 11
Sep 26, Best-match retrieval: Language models Ch 12
Sep 28, Query structure: Information needs and queries Nguyen & Callan, 2011
Oct 3, Query structure: Relevance and pseudo relevance feedback
HW2 due, HW3 out
Ch 9
Oct 5, Index creation Ch 4
Oct 10, Index creation
Document priors
Ch 7
Oct 12, Index creation
Oct 17, Document structure Ch 10
Oct 19, Midterm Exam Sample Midterm 1, Sample Midterm 2
Oct 24, Ranked retrieval: Feature-based models
HW3 due, HW4 out
Clarke Ch 11.7; Li, 2011
oct 26, Authority metrics Ch 21
Oct 31, Page quality, web spam Santos, Ch 1-5
Nov 2, Diversity Santos, Ch 6-7
Nov 7, Diversity
HW4 due, HW5 out
Nov 9, Search log analysis Eickhoff et al, 2014
Nov 14, Search log analysis  
Nov 16, Personalization Bennett et al, 2012
Nov 21, Federated, aggregated, & vertical search
HW5 due
Si & Callan, 2003
Nov 28, Federated, aggregated, & vertical search Arguello & Diaz, 2013
Nov 30, No class  
Dec 5, Enterprise search  
Dec 7, Final exam Sample final
Accommodations for Students with Disabilities: If you have a disability and have an accommodations letter from the Disability Resources office, please discuss your accommodations and needs with the instructor as early in the semester as possible. The instructor 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, please 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 2017, Carnegie Mellon University.
Updated on August 18, 2017
Jamie Callan