Course Syllabus: LIS 4930: Introduction to Information Retrieval
Fall 2015
Instructor: Alon Friedman, PhD---Instructor at the School of Information
University of South Florida
Office: CIS 2026
Phone: 813-974-35
Email: [email protected]
COURSE DESCRIPTION: The rising demand for accessing data and information has generated the need for better
methodologies to access and store data. One of the problems in accessing any information
is how to retrieve different data files including text, images, sound, user input, etc. This
course will introduce the student to the fundamentals of Information Storage and
Retrieval Systems (ISRS). We will focus on the theory and core concepts of information
retrieval systems; introduce the basic principles of information representation, storage
formats and different processing, and retrieval techniques and query representation. The
course will also discuss social media and visualization retrieval techniques.
The student will gain an understanding of what is achievable using existing technologies
and deficient areas that warrant additional research. The course provides coverage of all
of the major aspects of information retrieval and has sufficient detail to allow students to
implement a simple information storage retrieval system.
COURSE OBJECTIVES:
1. Understand the principles of Information Storage Retrieval.
2. To compare and contrast Information Retrieval models.
3. To learn different techniques for query representation (online library catalog
vs. Google)
5. To introduce the current trends in information retrieval regarding visualization and
Big Data.
COURSE FORMAT:
This is an online lecture class that will be based on Canvas online system, where course
announcements, distributing reading materials, submitting assignments/project/final and
grading will take place. You are also encouraged to use the discussion board for
furthering discussion and sharing insights outside of the classroom.
THE TEXTBOOKS FOR THIS COURSE:
1. Heting Chu (2010). Information representation and retrieval in the digital age. NJ.
Information Today, Inc. ISBN 57387-172-9.
2.. Baeza-Yates, Ricardo and Berthier, Ribeiro-Neto (2011). Modern Information
Retrieval: the Concepts and Technology Behind Search. 2nd. Harlow, England: Pearson
Education. Also available online: http://www.mir2ed.org/
3. MacLeod, D. (2012) How to find out anything: from extreme Google searches to
scouring government documents, a guide to uncover ring anything about everyone and
everything. Prentice Hall Press. ISBN 978-0-7352-0467
LEARNING AND TEACHING STRATEGIES:
The course will be taught through:
> Lectures
> Student assignments
> Computer Learning Methodologies
> Software applications (Microsoft Access and R)
> Different text reading
STUDENT PERFORMANCE EVALUATION
A. Class Assignments 30%
B. Final Exam 40%
C. Individual Project 30%
COURSE GRADE EVALUATION
Homework:
Homework will be assigned after each class. Homework problems and questions will
reflect the course (and exam) content. The students will need to submit homework
assignments on time. All assignments must be printed and submitted at the beginning of
class. Cooperation among students is encouraged but copying from other students will be
easily identified and should be avoided. Please make sure that each answer you provide
includes references to what was covered in class.
Exam:
One final exam will be given. The exam will be closed book/note and will test assigned
readings and material discussed in class.
Student Project:
There will be an individual project. It will consist of collecting a large set of data and
analysis it and presenting the end result in visualization form. Be sure to follow the
instructor's directions for the project.
GRADE SCALE
The student grade will be awarded according to the following scale
94 - 100%
A
90 - 93%
A87 - 89%
B+
84 - 86%
B
80 - 83%
B77 - 79%
C+
70 - 76%
C
60 - 69%
D
Below 60% F
COURSE POLCIES: This course will be offered via USF’s online system Canvas. If you need help learning
how to perform various tasks related to this course, please logon to:
http://www.usf.edu/atle/technology/canvas.aspx . You may also contact USF’s IT
department at (813) 974-1222 or [email protected]
DISABILITY ACCESS:
The University of South Florida is committed to providing reasonable accommodations
for all persons with disabilities. This syllabus is available in alternate formats upon
request. Students with disabilities are responsible for registering with the USF Students
with Disabilities Services office in order to receive special accommodations and services.
Please notify me during the first week of class if a reasonable accommodation for a
disability is needed for this course. A letter from the USF Students with Disabilities
Services office must accompany this request.
ACADEMIC CONDUCT POLICY:
Academic dishonesty in any form will not be tolerated. If you are uncertain as to what
constitutes academic dishonesty, please consult the University of South Florida's Student
Handbook for further details. Violations of these rules will result in a record of the
infraction being placed in your file and receiving a zero on the work in question AT A
SPREADSHEETS:
This class requires the use of open spreadsheet software for some statistical calculations.
Students in the class are expected to have some familiarity with Open Source spreadsheet
R which is required to complete the class homework assignments. To download Open
Source R spreadsheets go to: http://www.r-project.org/
Time Schedule
Module # 1
Module # 2
Module # 3
Module # 4
Subject
Introduction to Information Retrieval
Introduction to Information Representation: indexing,
categorization, citation etc
Introduction to Metadata, Double Core, Resources Description
Framework (RDF), Multimedia, etc.
Introduction to Controlled Vocabulary (Thesauri, subject heading,
classification schemes, etc) vs. natural language
Introduction to Database
Introduction to Retrieval techniques
Introduction to Query Representation
The User in information Retrieval
Introduction to Big Data Search Techniques
Introduction to Visualization retrieval
Introduction to Artificial intelligence
Module # 5
Module # 6
Module # 7
Module # 8
Module # 9
Module # 10
Module # 11
**Final Exam
The syllabus is subject to change
References:
Berners-Lee, Tim, James Hendler, and Ora Lassila (2001). "The Semantic
Web." Scientific American 284, no. 5: 35-43.
Bilal, Dania. (2012) "Ranking, Relevance Judgment, and Precision of Information
Retrieval on Children's Queries: Evaluating Google, Yahoo!, Bing, Yahoo! Kids, and
AskKids." Journal of the American Society for Information Science & Technology 63,
no. 9: 1879-1896.
Birmingham, William et al. (2002) "The MusArt Music Retrieval System." D-Lib
Magazine. Available at: www.dlib.org/dlib/february02/birmingham/02birmingham.html
Buttcher, Stefan, Charles L. A. Clarke, and Gordon V. Cormack. (2010) "Information
Retrieval: Implementing and Evaluating Search Engines." Cambridge, Mass: MIT Press,.
Case, Donald O. (2007) Looking for Information: A Survey of Research on Information
Seeking, Needs, and Behavior. 2nd ed. Boston: Academic Press.
Downie, Stephen J. (2003) "Music Information Retrieval." Annual Review of
Information Science and Technology (2003): 295-340.
Enser, Peter. (2008) "The Evolution of Visual Information Retrieval." Journal of
Information Science 34, no. 4: 531-546.
Goodrum, Abby and Edie Rasmussen. (2000) "Sound and Speech in Information
Retrieval: An Introduction." Bulletin of the American Society for Information Science
26, no. 5: 16-18.
Jorgensen, Corrine. (2009) Image Retrieval: Theory and Research. Lanham, MD:
Scarecrow Press, 2003.
Noruzi, Alireza. "Folksonomies: (UN) Controlled Vocabulary?" Knowledge
Organization 33, no. 4 (2006): 199-203.
Spink, Amanda, Detmar Wolfram, B. J, Jansen, & Tefko Saracevic. (2001) Searching the
Web: The Public and Their Queries." Journal of the American Society for Information
Science 53, no. 2: 226-234.
Zhang, J. (2007). Visualization for Information Retrieval. Springer Publication ISBN:
3540751483