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 pre-requisites.
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, NSH 1305.
Instructor: Jamie Callan
Teaching Assistants: Chenyan Xiong (cx@cs),
Zhoucheng Li (chouclee@cmu),
Manu Reddy Nannuri (mnannuri@cs),
Suruchi Shah (suruchis@cs),
Rui Wang (ruiw1@andrew)
Office hours:
Monday, 5:00-6:00, GHC 5417, Manu
Tuesday, 5:00-6:00, GHC 5417, Rui
Wednesday, 11:00-12:00, GHC 5417, Suruchi
Thursday, 4:30-5:30, GHC 5401, Chenyan
Friday, 3:00-4:00, GHC 5417, Zhoucheng
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 will need a Piazza account to use the discussion forum. Please provide a CMU email address when you join the 11-642 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: 5 homework assignments (12% each, 60% 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 13, Course overview (pdf)
Jan 15, Introduction to search: Exact-match retrieval (pdf) Ch 1, Ch 5.1
Jan 20, Introduction to search: Indexes, query processing (pdf)
HW1 out
Ch 2.4
Jan 22, Evaluating search effectiveness (pdf) Ch 8-8.5
Jan 27, Evaluating search effectiveness (pdf)
Jan 29, Document representation (pdf) 2-2.2
Feb 3, Best-match retrieval: VSM, BM25 (pdf)
HW1 due, HW2 out
Ch 6, Ch 11
Feb 5, Best-match retrieval: Language models (pdf) Ch 12
Feb 10, Query structure: Information needs and queries (pdf)
Feb 12, Query structure: Relevance and pseudo relevance feedback (pdf) Ch 9
Feb 17, Query structure: Relevance and pseudo relevance feedback (pdf)
Document structure (pdf)
HW2 due, HW3 out
Ch 7
Feb 19, Document structure (pdf)
Index creation (pdf)
Ch 10
Feb 24, Index creation (pdf) Ch 4
Feb 26, Index creation (pdf)
Retrieval optimization (pdf)
Mar 3, Midterm Exam Sample midterm 1, Sample midterm 2,
Midterm 1 answers, Midterm 2 answers
Mar 5, Index creation (pdf)  
Mar 17, Ranked retrieval: Feature-based models (pdf)
HW3 due
Clarke Ch 11.7; Li, 2011
Mar 19, Authority metrics (pdf)
HW4 out
Ch 21
Mar 24, Page quality, web spam (pdf)  
Mar 26, Diversity (pdf) Santos, Ch 1-5
Mar 31, Diversity (pdf)
HW4 due, HW5 out
Santos, Ch 6-7
Apr 2, Search log analysis (pdf)  
Apr 7, Search log analysis (pdf) Eickhoff et al, 2014
Apr 9, Personalization (pdf, pdf) Bennett et al, 2012
Apr 14, Federated, aggregated, & vertical search (pdf, pdf) Si & Callan, 2003
Apr 21, Federated, aggregated, & vertical search (pdf)
HW5 due
Arguello & Diaz, 2013
Apr 23, Selective search (pdf) Kulkarni & Callan, 2010
Apr 28, Enterprise search (pdf)  
Apr 30, Web crawling (pdf) Ch 20-20.2
May 5 Final exam, 5:30-8:10, Adamson Wing BH 136A Sample final

Copyright 2015, Carnegie Mellon University.
Updated on May 05, 2015
Jamie Callan