COMPSCI 546: Applied Information Retrieval (Spring 2021)

COMPSCI 546 is a graduate level course intended to cover information retrieval and other information processing activities, from an applied perspective. There will be multiple programming projects, as well as short answer homeworks. It provides a richer technical follow on to COMPSCI 446 (Search Engines), for undergraduates interested in a deeper understanding of the technologies. It also provides a strong basis for continuing on with COMPSCI 646 (Information Retrieval), for those graduate students who are interested in a more complete theoretical coverage of the area. Topics will include: search engine construction (document acquisition, processing, indexing, and querying); learning to rank; information retrieval system performance evaluation; classification and clustering; other machine learning information processing tasks; and many more.

Time: Tue & Thu, 10:00 - 11:15 AM

Location: Virtual!

Instructor

Hamed Zamani

Contact: zamani@cs.umass.edu

Office Hour: Tue 11:30 - 12:30 & Thu 16:00 - 17:00

Teaching Assistant

Lakshmi Vikraman

Contact: lvnair@cs.umass.edu

Office Hour: Mon 16:00 - 17:00 & Wed 11:00 - 12:00

Prerequisites

Textbook

Grading

Tentative Schedule

# Lecture Date Readings Note
1 Tue Feb 2 Introduction to IR
  • [WBC] Ch.1
  • [WBC] Ch.7.1
  • [CDM] Ch.8.1, 8.2
2 Thu Feb 4
3 Tue Feb 9 IR Evaluation and Ranking Metrics
4 Thu Feb 11 Assignment 1 - IR Metrics (deadline: Feb 18)
5 Tue Feb 16 Text Processing
  • [WBC] Ch.4.1, 4.2, 4.3
6 Thu Feb 18 Indexing
  • [WBC] Ch.5.1, 5.2, 5.3, 5.4, 5.7
Assignment 2 - Text Processing and Indexing (deadline: Mar 2)
7 Tue Feb 23 Basic Retrieval Models
8 Thu Feb 25
9 Tue Mar 2 Language Models for IR If you are interested in learning more about language modeling for IR, the book "Statistical Language Models for Information Retrieval" by ChengXiang Zhai is recommended.
10 Thu Mar 4 Assignment 3 - Retrieval Models (deadline: Mar 11)
11 Tue Mar 9 Query Expansion and Relevance Feedback
12 Thu Mar 11 Web Search Assignment 4 - Query Expansion (deadline: Mar 18)
13 Tue Mar 16 Search Engine Technologies
14 Thu Mar 18 Interactive IR Assignment 5 - Link Analysis (deadline: Mar 30)
15 Tue Mar 23 Question Answering
16 Thu Mar 25 Machine Learning Basics
17 Tue Mar 30 Document Clustering Assignment 6 - Clustering (deadline: Apr 6)
18 Thu Apr 1 Document Classification
19 Tue Apr 6 Learning to Rank Assignment 7 - Learning to Rank (deadline: Apr 20)
20 Thu Apr 8
21 Tue Apr 13 Neural Information Retrieval If you are interested in learning more about neural information retrieval, the book "An Introduction to Neural Information Retrieval" by Mitra and Craswell is recommended.
22 Thu Apr 15
Tue Apr 20 No class (Wednesday schedule)
23 Thu Apr 22 Recommender Systems
24 Tue Apr 27 Assignment 8 - Collaborative Filtering (deadline: May 4)
25 Thu Apr 29 IR Applications
26 Tue May 4 IR Research

Collaboration and Help

You may discuss the ideas behind assignments with others. You may ask for help understanding class and IR concepts. You may study with friends. However...

The work that you submit must be your own. It may not be copied from the web, from another student in the class, or from anyone else. If you stumble upon and use a solution from the textbook or from class, you are expected to acknowledge the source of the work.

Your effort on exams (mini or final) must be your own. Your homework submissions must be your own work and not in collaboration with anyone. Your project work must be your own work and not a copy of someone else's work, nor done in collaboration with anyone.

Last update: Jan. 10, 2021