George Mason University

Department of Computer Science

INFS 623: Web Search Engines and Recommender Systems

Fall 2016

Prof. Ami Motro

Description

This graduate course covers two hugely popular types of systems: Web search engines and recommender systems. Web search engines attempt to locate information items (e.g., documents, Web pages) based on user specifications. These systems have developed from traditional systems for bibliogrphic information retrieval dating back 50 years, and, with the exception of Web browsers, arguably are the most widely-used tool for accessing information on the Web. Recommeder systems discover information items (e.g., people, products) that are likely to be of interest to users. This coure will explore both types of systems, underlining their shared principles. Roughly, two thirds of the course will be devoted to search engines and one third to recommender systems.

Approximate class schedule
  1. Introduction to search engines and recommender systems (1/2 week)
  2. The Boolean and vector models of information retrieval (3 weeks)
  3. Advanced topics in search engines (2 weeks)
  4. Web-specific search engine methods (2 weeks)
  5. Recommender systems (3 weeks)
Time and place

Thursday, 4:30-7:10 pm
AB (Art and Design) 2003

Instructor

Dr. Ami Motro
Office: ENG-4415
Telephone: 703-993-1665
Email: ami@gmu.edu
Web: http://cs.gmu.edu/~ami
Office hours: Tuesday and Thursday, 3:00-4:00 pm

Prerequisites

The four foundation courses of the INFS, SWE and ISA Master's programs:

  1. SWE 510: Object-oriented Programming
  2. INFS 501: Discrete and Logical Structures
  3. INFS 515: Computer Organization
  4. INFS 519: Program Design and Data Structures
Students from the CS Master's and PhD programs with full degree status (i.e., non-provisional) are waived from these prerequisites. Students from other programs (including provisional or non-degree students) must provide evidence of having taken courses equivalent to these foundation courses.
Prerequisites are strictly enforced!

Requirements

Two exams (a mid-term and a final) and 5 homework assignments.
The final grade would be based on exams (37.5% + 37.5%) and homework assignments (25%).

Textbooks
  1. Introduction to Information Retrieval
    Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schutze
    Cambridge University Press, 2008
  2. Recommender Systems: An Introduction
    Dietmar Jannach, Marcus Zanker, Alexander FelFering, and Gerhard Friedrich
    Cambridge University Press, 2011