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

TBD

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

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Prerequisites

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

Textbook

• [WBC] W. Bruce Croft, Donald Metzler, and Trevor Strohman. Search Engines: Information Retrieval in Practice. Pearson Education, 2009.
• [CDM] Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. Introduction to Information Retrieval. Cambridge University Press, 2008.

 

Grading

• 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	[CDM] Ch.8.3, 8.4 (optional) NDCG: Järvelin and Kekäläinen (TOIS 2002)
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	[CDM] Ch.6.2, 6.3, 6.4 [CDM] Ch.11.1, 11.2, 11.3, 11.4.3 (optional) LSI: Deerwester et al. (JASIS 1990)
8		2/29
9	Language Models for IR	3/5	[CDM] Ch.12.1, 12.2, 12.3, 12.4 [WBC] Ch.11.3 (optional) Query likelihood: Ponte and Croft (SIGIR 1998) (optional) KL-divergence retrieval model: Lafferty and Zhai (SIGIR 2001) (optional) Language model smoothing: Zhai and Lafferty (TOIS 2004) (optional) Cluster-based LM: Liu and Croft (SIGIR 2004) (optional) Dependence models: Metzler and Croft (SIGIR 2005) (optional) Positional LM: Lv and Zhai (SIGIR 2009)	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	[WBC] Ch.7.3.2 (optional) Relevance models: Lavrenko and Croft (SIGIR 2001) (optional) Model-based feedback: Zhai and Lafferty (CIKM 2001) (optional) Matrix factorization model: Zamani et al. (CIKM 2016)
12	Web Search and Search Engine Technologies	3/14	[CDM] Ch.21 (optional) PageRank: Page et al. (1998) (optional) HITS: Kleinberg (JACM 1999) (optional) Waterloo spam scorer: Cormack et al. (IRJ 2011)	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	Maximal Marginal Relevance (MMR): Carbonell and Goldstein (SIGIR 1998) Novelty and diversity evaluation: Clarke et al. (SIGIR 2008) (optional) Subtopic retrieval using language models: Zhai et al. (SIGIR 2003) (optional) Term-level diversification: Dang and Croft (SIGIR 2013)	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	LTR Textbook: Li (2011) Ch.1, 2, 6 (optional) LTR Textbook: Li (2011) Ch.4
20	Introduction to Neural Networks and Neural IR	4/11
21	Distributed Representation Learning for Text	4/16	word2vec: Mikolov et al. (NIPS 2013) (optional) GloVe: Pennington et al. (EMNLP 2014) (optional) Relevance-based Word Embedding: Zamani and Croft (SIGIR 2017) (optional) Embedding-based Query Expansion: Zamani and Croft (ICTIR 2016)	Assignment 7 (Learning to Rank)
22	Neural Ranking Models	4/18	(optional) DSSM: Huang et al. (CIKM 2013) (optional) K-NRM: Xiong et al. (SIGIR 2017) (optional) Duet Model: Mitra et al. (WWW 2017) (optional) SNRM: Zamani et al. (CIKM 2018)
23		4/23
24	Question Answering	4/25
25	Information Filtering and Recommendation	4/30	(optional) Information Retrieval and Information Filtering: Belkin and Croft (CACM 1992) (optional) Recommender Systems Handbook: Ricci et al. (2015)
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

• UMass Academic Calendar
• Academic Honesty Policy
• English as a Second Language (ESL) Program
• Center for Counseling and Psychological Health
• Disability Services