Search Engines:
11-442 / 11-642
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Description: This 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:
Jing Chen (jingc1@cs)
Zhuyun Dai (zhuyund@cs)
Deepak Gopinath (dgopina1@andrew)
Weijia (Amber) Li (weijial@andrew)
Hongyu Li (hongyul@andrew)
Yuqi (Sarah) Liu (yuqil@andrew)
Vallari Mehta (vallarim@andrew) Handles reading summaries
Office hours:
Day Time Location TA
Monday 4:30-6:00 GHC 6404 Hongyu
Tuesday 1:00-2:30
GHC 5417
GHC 5417
Wednesday 11:00-12:30 GHC 5417 Deepak
Thursday 1:00-2:30 GHC 5417 Zhuyun
Friday (after Sep 6) 11:00-12:30 GHC 6708 Weijia
Instructional 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.
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.
Course policies: Attendance, Auditing, Laptops & mobile devices, Late homework, Pass/Fail, Plagiarism & cheating, Recording & videotaping, Waitlist
(subject to revision):
Date Topic Readings
Aug 30, Course overview (pdf)
Sep 1, Introduction to search: Exact-match retrieval (pdf)
Reading summaries (pdf)
Ch 1, Ch 5.1
Sep 6, Introduction to search: Query processing (pdf)
HW1 out
Ch 2.4
Sep 8, Introduction to search: Query processing (pdf)
Software development guidelines (pdf)
Sep 13, Evaluating search effectiveness (pdf) Ch 8-8.5
Sep 15, Evaluating search effectiveness (pdf)  
Sep 20, Document representation - recorded lecture (part I, part II) (pdf)
HW1 due, HW2 out
Ch 2-2.2
Sep 22, Best-match retrieval: VSM, BM25 (pdf) Ch 6, Ch 11
Sep 27, Best-match retrieval: Language models (pdf) Ch 12
Sep 29, Query structure: Information needs and queries (pdf) Nguyen & Callan, 2011
Oct 4, Query structure: Relevance and pseudo relevance feedback (pdf)
HW2 due, HW3 out
Ch 9
Oct 6, Index creation (pdf)  
Oct 11, Index creation (pdf),
Document priors (pdf)
Ch 4, 7
Oct 13, Index creation (pdf)
Oct 18, Midterm Exam Sample Midterm 1, Sample Midterm 2
Oct 20, Document structure (pdf) Ch 10 (No reading summary this week)
Oct 25, Ranked retrieval: Feature-based models (pdf)
HW3 due, HW4 out
Clarke Ch 11.7; Li, 2011
Oct 27, Authority metrics (pdf),
Page quality, web spam (pdf)
Ch 21
Nov 1, Page quality, web spam
Diversity (pdf)
Santos, Ch 1-5
Nov 3, Diversity (pdf) Santos, Ch 6-7
Nov 8, Search log analysis (pdf)
HW4 due, HW5 out
Nov 10, Search log analysis (pdf) Eickhoff et al, 2014
Nov 15, No class  
Nov 17, No class  
Nov 22, Personalization (pdf)
HW5 due
Bennett et al, 2012
Nov 29, Federated, aggregated, & vertical search (pdf) Si & Callan, 2003
Dec 1, Federated, aggregated, & vertical search (pdf) Arguello & Diaz, 2013
Dec 6, Enterprise search (pdf)  
Dec 8, Final exam Sample final
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 2016, Carnegie Mellon University.
Updated on December 06, 2016
Jamie Callan