COMPSCI 546: Applied Information Retrieval – Spring 2024

COMPSCI 546 is a graduate level course intended to cover information retrieval and other information processing activities, from an applied perspective. There will be numerous programming projects and assignments. 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 (e.g., basic deep learning models for information retrieval); and many more. Undergraduate prerequisites: COMPSCI 320 and either COMPSCI 383, COMPSCI 446, or COMPSCI 585. 3 credits.

Tue & Thu, 4:00 – 5:15 PM

CS 142

Hamed Zamani - UMass Amherst

Hamed Zamani
Office Hours: Tue, 9:00 – 10:00 AM @ CS 350

Teaching Assistant:
Hasnain Heickal
Office Hours: Mon, 9:00 – 11:00 AM @ CS 207


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




  • Assignments ((8-1)x10%) – Your lowest grade assignment would be discarded.
  • Final exam (30%)


Tentative Schedule

# Lecture Date Readings Note
1 Introduction 2/1
  • [WBC] Ch.1
  • [WBC] Ch.7.1
  • [CDM] Ch.8.1, 8.2
2 IR Basics 2/6
3 IR Evaluation and Ranking Metrics 2/8  
4 2/13 Assignment 1 (IR Metrics)
5 Text Processing and Indexing 2/15
  • [WBC] Ch.4.1, 4.2, 4.3
  • [WBC] Ch.5.1, 5.2, 5.3, 5.4, 5.7
6 2/20 Assignment 2 (Text Processing & Indexing)
  No Class (Monday Class Schedule) 2/22    
7 Basic Retrieval Models 2/27  
8 2/29
9 Language Models for IR 3/5 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 3/7 Assignment 3 (Retrieval Models)
11 Query Expansion and Relevance Feedback 3/12  
12 Web Search and Search Engine Technologies 3/14 Assignment 4 (Query Expansion)
  No Class (Spring Break) 3/19    
  No Class (Spring Break) 3/21    
13 Web Search and Search Engine Technologies (cont’d) 3/26    
16 Novelty and Diversity 3/28

Assignment 5 (Link Analysis)

17 Machine Learning Basics 4/2



18 Document Clustering and Classification 4/4


Assignment 6 (Clustering)
19 Learning to Rank 4/9  
20 Introduction to Neural Networks and Neural IR 4/11    

Distributed Representation Learning for Text

4/16 Assignment 7 (Learning to Rank)
22 Neural Ranking Models 4/18  
23 4/23
24 Question Answering 4/25    
25 Information Filtering and Recommendation 4/30  
26 5/2 Assignment 8 (Collaborative Filtering)
27 IR Applications 5/7    
28 IR Research 5/9    


Course Policy

Late Submission

Each student has a total of 5 late days without penalty. You can use up to 3 late days per assignment. 

Once all 5 late days are used, any assignments turned in late will be penalized 20% (absolute grade) 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 the final exam must be your own. Your assignment submissions must be your own work and not in collaboration with anyone. 

Relevant UMass Resources