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CS499/AI539: Information Retrieval – Spring 2023
Course Description
This course focuses on the foundations of modern search engine and advanced techniques of text-based information systems, including indexing, query understanding, learning to rank, interactive search, evaluation and user models, question answering, conversational system, and neural models for IR. The course also covers current research topics of information retrieval.
Course Information
Lectures
Tuesday & Thursday 4:00-5:50pm at Bexell Hall 417
Instructor
Huazheng Wang
Email: huazheng.wang [at] oregonstate.edu
Office: KEC 3097
Office hours: Tuesday 2-4pm
Contact
We will use Canvas for posting slides and assignments, and Discord for communication. See Canvas announcements for the link to Discord channel.
Prerequisites
Python. We will use Python/Jupyter notebook for programming assignments.
Basic knowledge of probability and statistics.
Basic understanding of machine learning / deep learning.
Schedule
| Week | Date | Lecture | Readings | Paper Presentation |
| Week 1 | 4/4 | Introduction to the course | |
| 4/6 | Information retrieval basics | [MRS] Ch 1. | |
| Week 2 | 4/11 | Web crawling and text processing | [MRS] Ch 2, Ch 20. | |
| 4/13 | Inverted index and index construction | [MRS] Ch 4. | |
| Week 3 | 4/18 | IR evaluations and metrics | [MRS] Ch 8. | |
| 4/20 | Modern IR evaluations | [MRS] Ch 8. | |
| Week 4 | 4/25 | Vector space models and probabilistic retrieval models | [MRS] Ch 6, Ch 11. | |
| 4/27 | Language models | [MRS] Ch 12. | |
| Week 5 | 5/2 | Machine learning basics, text classification and clustering | [MRS] Ch 13, Ch 14. | |
| 5/4 | Learning to rank | | |
| Week 6 | 5/9 | Relevance feedback, implicit feedback and click model | | |
| 5/11 | Neural networks and neural information retrieval | | |
| Week 7 | 5/16 | Distributed representation learning for text | | |
| 5/18 | Neural ranking models | | |
| Week 8 | 5/23 | Link analysis | | |
| 5/25 | Question answering | | |
| Week 9 | 5/30 | Conversational system | | |
| 6/1 | Interactive information retrieval and online learning | | |
| Week 10 | 6/6 | Project presentations | | |
| 6/8 | Project presentations | |
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Gradings
Homework – (3*15%) 45%
Midterm exam – 20%
Paper presentation – 10%
Final project – 25%
Total – 100%
Paper presentation is required for graduate students and optional for undergraduate students. If choose not to present a paper, final project will be 35%.
Resources
Readings:
[MRS] Introduction to Information Retrieval. Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schuetze, Cambridge University Press, 2008.
[CMS] Search Engines: Information Retrieval in Practice. Bruce Croft, Donald Metzler, and Trevor Strohman, Pearson Education, 2009.
[Zhai] Statistical Language Models for Information Retrieval. ChengXiang Zhai, Morgan & Claypool Publishers, 2008.
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