CS 547/DS 547: Information Retrieval :: Fall 2025

Course readings will be drawn from the following resources:

• (IIR) Introduction to Information Retrieval (2008). Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schutze, Cambridge University Press.
• (NIR) Introduction to Neural Information Retrieval (2019). Bhaskar Mitra and Nick Craswell, Now Publishers.

• (MMD) Mining of Massive Datasets (2020). Jure Leskovec, Anand Rajarman and Jeffrey D. Ullman. Cambridge University Press.
• Research papers selected by the instructor

Tentative Schedule (subject to change)

Class #	Date	Topic	Readings	Homework	Project
1	8/21	Course overview, Text retrieval basics, boolean retrieval	IIR 1
2	8/28	boolean retrieval; Improving the index	IIR 2.1-2.4, 3.1-3.2	hw1 out
3	9/11	Improving the index; Vector space retrieval	IIR 6		Notify names of your project team members by September 11
4	9/18	Quiz; Vector space retrieval; BM25; Statistical language models; Web Search Basics	IIR 7, 12, 19.1-19.4, 20, A Standard for Robot Exclusion, Public API list	hw2 out
5	9/25	Web Search Basics; Link analysis (PageRank and HITS); Evaluating the retrieval engine	MMD 5.1, Google Bomb, IIR 21.1-21.2, PageRank IIR 21.3, MMD 5.5, HITS (Wikipedia), Kleinberg's original paper, IIR 8
6	10/2	Review; Recommenders, PyTorch Tutorial	MMD 9, Recommender System (Wikipedia)	hw3 out
7	10/9	Midterm
8	10/23	Recommenders; Project workday	Matrix Factorization Techniques for Recommender Systems, Netflix Prize, How the Netflix Prize was won Collaborative filtering with temporal dynamics, 2009. MMD 9, Recommender System (Wikipedia)		Project proposal submission by October 24
9	10/30	Proposal Presentation; Classification	IIR 13, 14		Proposal Presentation slides by October 29
10	11/6	Classification		hw4 out
11	11/13	Quiz; Learning to Rank; Neural Methods for IR; Distributed word representations for IR; LLM for IR	IIR 15.4, Wikipedia: Learning to Rank, Learning to Rank for Information Retrieval; An Introduction to Neural Information Retrieval (mainly Ch 2.6 and 3); SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval, 2020; Distributed Representations of Words and Phrases and their Compositionality, 2013; A Deep Relevance Matching Model for Ad-hoc Retrieval, 2016; A Dual Embedding Space Model for Document Ranking, 2016 Large Language Models for Information Retrieval: A Survey, 2024
12	11/20	Advanced Recommender System	Regularizing Matrix Factorization with User and Item Embeddings for Recommendation, 2018; Neural Collaborative Filtering, 2017; Neural Graph Collaborative Filtering, 2019; RecExplainer: Aligning Large Language Models for Explaining Recommendation Models, 2024
13	12/4	Final exam
14	12/11	Project Presentation			Project deliverables due: Dec 10