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 1:30-2:50, DH A302
Instructor: Jamie Callan
Teaching Assistants:
Claire Zhiyue Liu (zhiyuel@andrew)
Subodh Asthana (sasthana@andrew) Handles reading summaries
Weijia (Amber) Li (weijial@andrew)
Supriya Vijay (supriyav@andrew)
Shashank Shivakumar (sshivak1@andrew)
Yu Qin (yqin1@andrew)Handles reading summaries
Yue Zhou (yzhou3@andrew)
Office hours:
Day Time Location TA
Monday 11:00-12:30
GHC 5417
GHC 5417
Tuesday 12:00-1:30 GHC 5417 Supriya
Thursday   3:00-4:30 GHC 5417 Shashank
Friday   5:00-6:30 GHC 5417 Zhiyue
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
Jan 12, Course overview (pdf)
Jan 14, Introduction to search: Exact-match retrieval (pdf) Ch 1, Ch 5.1
Jan 19, Introduction to search: Indexes, query processing (pdf)
HW1 out
Ch 2.4
Jan 21, Introduction to search: Document-at-a-time retrieval (pdf)  
Jan 26, Evaluating search effectiveness (pdf) Ch 8-8.5
Jan 28, Evaluating search effectiveness (pdf)  
Feb 2, Document representation (pdf)
HW1 due, HW2 out
Ch 2-2.2
Feb 4, Best-match retrieval: VSM, BM25 (pdf) Ch 6, Ch 11
Feb 9, Best-match retrieval: Language models (pdf) Ch 12
Feb 11, Query structure: Information needs and queries (pdf) Nguyen & Callan, 2011
Feb 16, Query structure: Relevance and pseudo relevance feedback (pdf)
HW2 due, HW3 out
Ch 9
Feb 18, Document structure (pdf) Ch 10
Feb 23, Document priors (pdf)
Index creation (pdf)
Ch 4
Feb 25, Index creation (pdf) Ch 7
Mar 1, Midterm Exam Sample Midterm 1, Sample Midterm 2
Mar 3, Index creation (pdf)  
Mar 15, Ranked retrieval: Feature-based models (pdf)
HW3 due, HW4 out
Clarke Ch 11.7; Li, 2011
Mar 17, Authority metrics (pdf) Ch 21
Mar 22, Page quality, web spam (pdf)  
Mar 24, Diversity (pdf) Santos, Ch 1-5
Mar 29, Diversity (pdf)
HW4 due, HW5 out
Santos, Ch 6-7
Mar 31, Search log analysis (pdf)  
Apr 5, Search log analysis (pdf) Eickhoff et al, 2014
Apr 7, Personalization (pdf) Bennett et al, 2012
Apr 12, Federated, aggregated, & vertical search (pdf)
HW5 due
Si & Callan, 2003
Apr 19, Federated, aggregated, & vertical search (pdf) Arguello & Diaz, 2013
Apr 21, Selective search (pdf) Kulkarni & Callan, 2010
Apr 26, Enterprise search (pdf)  
Apr 28, Final exam Sample final

Copyright 2016, Carnegie Mellon University.
Updated on April 26, 2016
Jamie Callan