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

Proficiency in Java is highly recommended.

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 (3x10%)
Midterm exam (25%): Friday, October 26, 2018, 19:00 - 21:00
Final exam, take home (20%)
Final project (25%)

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	[WBC] Ch.1 [WBC] Ch.7.1 [CDM] Ch.8.1, 8.2
3	Evaluation	Wed. 9/12	[CDM] Ch.8.3, 8.4 (optional) NDCG: Järvelin and Kekäläinen (TOIS 2002)
	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	[CDM] Ch.6.2, 6.3, 6.4 [CDM] Ch.18 (optional) LSI: Deerwester et al. (JASIS 1990)
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	[CDM] Ch.12.1, 12.2, 12.3 (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)	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	[WBC] Ch.11.3 [CDM] Ch.12.4 (optional) Cluster-based LM: Liu and Croft (SIGIR 2004) (optional) LDA-based LM: Wei and Croft (SIGIR 2006) (optional) Local document expansion: Tao et al. (NAACL 2006) (optional) General LM: Song and Croft (CIKM 1999) (optional) HMM IR: Miller et al. (SIGIR 1999) (optional) Dependence models: Metzler and Croft (SIGIR 2005) (optional) Positional LM: Lv and Zhai (SIGIR 2009)
10	Relevance Feedback	Wed. 10/10	[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)
11	Learning to Rank	Mon. 10/15	LTR Textbook: Li (2011) Ch.1, 2, 6 (optional) LTR Textbook: Li (2011) Ch.4
12	Implicit Feedback, Biases, and Click Models	Wed. 10/17	(optional) Implicit feedback: Joachims et al. (SIGIR 2005) (optional) Click models for web search: Chuklin et al. (2015) (optional) Counterfactual learning: Joachims and Swaminathan (SIGIR 2016) (optional) Unbiased learning to rank: Joachims et al. (WSDM 2017)
13	Link Analysis & Spam Filtering for Web Search	Mon. 10/22	[CDM] Ch.21 (optional) PageRank: Page et al. (1998) (optional) HITS: Kleinberg (JACM 1999) (optional) Waterloo spam scorer: Cormack et al. (IRJ 2011)
14	Context-Awareness and Personalization in Search	Wed. 10/24	(optional) Context-sensitive language model: Shen et al. (SIGIR 2005) (optional) Web search personalization: Bennett et al. (SIGIR 2012) (optional) Situational context for search: Zamani et al. (WWW 2017)
15	Novelty and Diversity	Mon. 10/29	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)
16	User Study and Crowdsourcing in IR	Wed. 10/31	Crowdsourcing user studies with Mechanical Turk: Kittur et al. (CHI 2008) (optional) Interactive IR evaluation: Kelly et al. (ICTIR 2015) (optional) Understanding user behaviour from query logs: Dumais et al. (Ways of Knowing in HCI, 2014)
17	Information Filtering and Recommendation	Mon. 11/5	(optional) Information Retrieval and Information Filtering: Belkin and Croft (CACM 1992) (optional) Recommender Systems Handbook: Ricci et al. (2015)
18	RecSys (Cont'd) & Neural Network Basics	Wed. 11/7
	Veteran's Day	Mon. 11/12
19	Introduction to Neural Networks for IR & Word Embedding	Wed. 11/14	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)
	Thanksgiving Holidays	Mon. 11/19
	Thanksgiving Holidays	Wed. 11/21
20	Neural Ranking Models	Mon. 11/26	(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)	If you are interested in learning more about neural information retrieval, the book "An Introduction to Neural Information Retrieval" by Mitra and Craswell is recommended.
21	Cross- and Multi-Lingual IR	Wed. 11/28
22	Question Answering	Mon. 12/3
23	IR Applications: Personal Search, Product Search, and Entity Search	Wed. 12/5
24	Fairness, Accountability, Transparency, and Ethics in IR	Mon. 12/10		Guest Lecture by Fernando Diaz.
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