COMPSCI 646: Information Retrieval (Fall 2018)

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.

Time: Mon & Wed, 9:05 - 10:20 AM

Location: CS 142

Instructors

Hamed Zamani (and James Allan)

Contact: zamani@cs.umass.edu

Office Hour: Mon & Wed, 11:00 - 12:00 @ CS 207

Teaching Assistant

Qingyao Ai

Contact: aiqy@cs.umass.edu

Office Hour: Tue & Thu, 16:00 - 17:00 @ CS 207

Prerequisites

Textbook

Grading

Tentative Schedule

# Lecture Date Readings Note
1 Introduction Wed. 9/5
  • [WBC] Ch.1
  • [WBC] Ch.7.1
  • [CDM] Ch.8.1, 8.2
2 IR Basics Mon. 9/10
3 Evaluation Wed. 9/12
No class Mon. 9/17
4 Text Processing and Indexing Wed. 9/19
  • [WBC] Ch.4.1, 4.2, 4.3
  • [WBC] Ch.5.1, 5.2, 5.3, 5.4, 5.7
5 Indexing (cont'd) & Vector Space Models (VSM) Mon. 9/24
6 VSM (cont'd) & Latent Semantic Indexing (LSI) Wed. 9/26
7 Probabilistic Retrieval Models Mon. 10/1
  • [CDM] Ch.11.1, 11.2, 11.3, 11.4.3
8 Language Modeling Wed. 10/3 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.
9 Enhanced Language Modeling (local smoothing and proximity-based models) Tue. 10/9
10 Relevance Feedback Wed. 10/10
11 Search Result Diversification Mon. 10/15
12 Learning to Rank Wed. 10/17
13 Implicit Feedback, Biases, and Click Models Mon. 10/22
14 Link Analysis & Spam Filtering for Web Search Wed. 10/24
15 Context-Awareness and Personalization in Search Mon. 10/29
16 User Study and Crowdsourcing in IR Wed. 10/31
17 Information Filtering and Recommendation Mon. 11/5
18 Introduction to Neural Networks for IR & Word Embedding Wed. 11/7
Veteran's Day Mon. 11/12
19 Neural Ranking Models Wed. 11/14
Thanksgiving Holidays Mon. 11/19
Thanksgiving Holidays Wed. 11/21
20 Learning from Limited Data for IR Mon. 11/26
21 IR Applications: Cross- and Multi-Lingual IR Wed. 11/28
22 IR Applications: Question Answering Mon. 12/3
23 IR Applications: Personal Search, Product Search, and Entity Search Wed. 12/5
24 IR Applications: Mobile IR & Conversational IR Mon. 12/10
25 Course Summary & Current IR Research Wed. 12/12

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: 2018/03/26