COMPSCI 646: Information Retrieval – Fall 2021

COMPSCI 646 is a graduate-level course in Information Retrieval, the science and engineering of indexing, organizing, searching, and making sense of unstructured or mostly unstructured information, particularly text. The class focuses primarily on the underlying models used for effective search and organization, but includes some discussion of efficiency concerns. The course also covers current research problems and methodologies in the field of Information Retrieval.

Tue & Thu, 4:00 – 5:15 PM

CS 142


Hamed Zamani - UMass Amherst

Instructor:
Hamed Zamani
Contact: zamani@cs.umass.edu
Office Hours: Tue & Thu, 9:00 – 10:00 AM @ CS 350

Teaching Assistant:
Lakshmi Vikraman
Contact: lvnair@cs.umass.edu
Office Hours: Mon & Wed, 10:00 – 11:00 AM @ LGRT 220 (T220)

Prerequisites

  • Proficiency in Python and/or Java
  • Basic knowledge of probability, statistics, and information theory
  • Foundations of applied machine learning and deep learning


Textbook


Grading (subject to change!)

  • Assignments (3×10%)
  • Midterm exam (25%): Friday, October 29, 2021, 7 – 9 PM
  • Final exam, take home (20%)
  • Final project (25%)


Tentative Schedule

# Lecture Date Readings
1 Introduction Thu 9/2
  • [WBC] Ch.1
  • [WBC] Ch.7.1
  • [CDM] Ch.8.1, 8.2
2 IR Basics Tue 9/7
3 IR Evaluation Methodologies, Metrics, and User Models Thu 9/9
4 Tue 9/14
5 Text Processing and Indexing Thu 9/16
  • [WBC] Ch.4.1, 4.2, 4.3
  • [WBC] Ch.5.1, 5.2, 5.3, 5.4, 5.7
6 Basic Retrieval Models: Vector Space Models & Probabilistic Retrieval Models Tue 9/21
  • [CDM] Ch.6.2, 6.3, 6.4
  • [CDM] Ch.11.1, 11.2, 11.3, 11.4.3
7 Language Modeling Thu 9/23
8 Enhanced Language Modeling Tue 9/28
9 Relevance Feedback Thu 9/30
10 ML Basics & Learning to Rank Tue. 10/5
11 Introduction to Neural Networks and Neural IR Thu 10/7  
12

Distributed Representation Learning for Text

Tue 10/12
13 Neural Ranking Models Thu 10/14
14 Tue 10/19
15 Implicit Feedback, Biases, and Click Models Thu 10/21
16 Web Search: Link Analysis, Spam Filtering, MapReduce Tue 10/26
17 Context-Awareness and Personalization in Search Thu 10/28
18 Novelty and Diversity Tue 11/2
19 User Study and Crowdsourcing in IR Thu 11/4
20 Cross- and Multi-Lingual IR Tue 11/9  
  No Class: Veteran’s Day Thu 11/11  
21 Information Filtering and Recommendation Tue 11/16
22 Thu 11/18
23 Entity Search Tue 11/23  
  No Class: Thanksgiving Thu 11/25  
24 Question Answering Tue 11/30  
25 Conversational Information Seeking Thu 12/2

 

26 Current IR Research Tue 12/7  

 

Course Policy

Late Submission

Each student has a total of 6 late days without penalty. You can use up to 3 late days per assignment or project milestone excluding project final report. Late submission of team assignments will result in each member of the team being charged for the late days. For example, if a group of two students submitted their project proposal 23 hours after the deadline, this results in 1 late day being used per student.

Once all 6 late days are used, any assignments turned in late will be penalized 20% per late day.

In case of multiple submissions of an assignment, only the last one will be taken into account for the number of late days.

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.

Relevant UMass Resources