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 the final exam.
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)
Weijia (Amber) Li (weijial@andrew)
Andrew Low (kahkhanl@andrew)
Vallari Mehta (vallarim@andrew>
Office hours:
Day Time Location TA
Monday TBD
Thursday TBD TBD TBD
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
Sep 1, Introduction to search: Exact-match retrieval Ch 1, Ch 5.1
Sep 6, Introduction to search: Indexes, query processing
HW1 out
Ch 2.4
Sep 8, Introduction to search: Document-at-a-time retrieval  
Sep 13, Evaluating search effectiveness Ch 8-8.5
Sep 15, Evaluating search effectiveness  
Sep 20, Document representation
HW1 due, HW2 out
Ch 2-2.2
Sep 22, Best-match retrieval: VSM, BM25 Ch 6, Ch 11
Sep 27, Best-match retrieval: Language models Ch 12
Sep 29, Query structure: Information needs and queries Nguyen & Callan, 2011
Oct 4, Query structure: Relevance and pseudo relevance feedback
HW2 due, HW3 out
Ch 9
Oct 6, Document structure Ch 10
Oct 11, Document priors, Index creation Ch 4
Oct 13, Index creation Ch 7
Oct 18, Midterm Exam Sample Midterm 1, Sample Midterm 2
Oct 20, Index creation  
Oct 25, Ranked retrieval: Feature-based models
HW3 due, HW4 out
Clarke Ch 11.7; Li, 2011
Oct 27, Authority metrics Ch 21
Nov 1, Page quality, web spam  
Nov 3, Diversity Santos, Ch 1-5
Nov 8, Diversity
HW4 due, HW5 out
Santos, Ch 6-7
Nov 10, Search log analysis  
Nov 15, Search log analysis Eickhoff et al, 2014
Nov 17, Personalization Bennett et al, 2012
Nov 22, Federated, aggregated, & vertical search
HW5 due
Si & Callan, 2003
Nov 29, Federated, aggregated, & vertical search Arguello & Diaz, 2013
Dec 1, Selective search Kulkarni & Callan, 2010
Dec 6, Enterprise search  
Dec 8, Final exam Sample final

Copyright 2016, Carnegie Mellon University.
Updated on June 03, 2016
Jamie Callan