1. Overview of IR Research
ChengXiang Zhai
Department of Computer Science
University of Illinois, Urbana-Champaign

2. What is Information Retrieval (IR)?
Salton’s definition (Salton 68): “information retrieval is a field concerned with the structure, analysis, organization, storage, searching, and retrieval of information”
Information: mostly text, but can be anything (e.g., multimedia)
Retrieval:
Narrow sense: search/querying
Broad sense: filtering, classification, summarization, ...
In more general terms
Information access
Information seeking
Help people manage and make use of all kinds of information

3. Who are working on IR? (IR and Related Areas)
Applications
Models
Applications
Web, Bioinformatics…
Machine Learning
Pattern Recognition
Data Mining
Human-Computer
Interaction
Library & Info
Science
Statistics
Optimization
Computer
Vision
Information
Retrieval
Databases
Natural
Language
Processing
Software engineering
Computer systems
Algorithms
Systems

4. IR and NLP
The two fields were closely related from day one, but somewhat disconnected later when NLP focused more on cognitive and symbolic approaches, while IR focused more on pure statistical approaches
Most recently the two fields regained close interactions
More complex retrieval tasks (question answering, opinons)
More scalable/robust NLP techniques (parsing, extraction)
IR researchers pioneered statistical approaches to NLP in 1950’s (e.g., H. P. Luhn), which only became popular in 1990’s among NLP researchers

5. IR and Databases
“Sibling” fields, but they didn’t get along with each other well
IR and DB share many common tasks, but the differences in the form of data and nature of queries are large enough to separate the two fields in most of the history
Major differences in data, user, query, what counts as answers: DB  efficiency; IR  effectiveness
The two fields are now getting closer and closer now (DB researchers realized the importance of 80% unstructured data, and IR researchers realized the importance of semantic search)

6. IR and Machine Learning
IR as a subfield of AI (IR=intelligent text access)?
AI is too big to have a coherent community (e.g., ML, NLP, Computer Vision all “spin off”)
IR researchers did machine learning as early as in 1960’s (Rocchio 1965, relevance feedback), but supervised learning didn’t get popular in IR until in early 1990’s when text categorization started getting a lot of attention
Lack of training data for search (no large-scale online system, users don’t like to make effort on judgments)
Learning-based approach didn’t prevail for ad hoc retrieval
Machine learning is now very important for IR

7. IR and Library & Information Science
Inseparable from day one (“Information Science” vs. “Computer Science”)
Early IR work was mostly done in the context of library and information science (LIS)
I-School initiative/movement: drop “library” and enlarge the scope to “informatics”, leading to merger of CS + LIS
Another example where the boundary between fields is disappearing (setting boundaries is generally harmful for research, but is sometimes needed in practice)

8. IR and Software Engineering
Scalability of IR wasn’t a major concern until the Web
Data collection was relatively small and didn’t grow quickly until the Web
The most effective retrieval models remain simple models based on bag-of-words representation
However, scalability has always been a core issue in IR, and how to engineer an IR system optimally is extremely important for IR applications
Nowadays, data-intensive computing is essential for large-scale IR applications

9. IR and Applications Early days: library search, literature
1970s: small-scale online search systems
1990s: large-scale systems
TREC (mostly news data, later other kinds of data)
Web search engines
2010s: search is everywhere!
More and more applications in the future

10. Publications/Societies (broad view)
Learning/Mining
Applications
ICML
ISMB
WWW
ICML, NIPS, UAI
WSDM
RECOMB, PSB
ACM SIGKDD
Info. Science
ICDM, SDM
Info Retrieval
Statistics
JASIS
JCDL
ACM SIGIR
AAAI
HLT
NLP
ECIR, CIKM, TREC
TOIS, IRJ, IPM
Databases
ACL
ACM SIGMOD,VLDB
COLING, EMNLP, NAACL
OSDI
ICDE, EDBT, TODS
Software/systems

11. Major IR Publication Venues
2010
<1960
1970
1980
1990
2000
ACM SIGIR
1978
CIKM
1994
ECIR
1978
WWW
1994
WSDM
2008
TREC
1992
ACM TOIS
1983
IMP(ISR)
1965
IRJ
1998
JASIST
1950
JDoc
1945

12. IR Research Topics (Broad View)
Users
Retrieval
Applications
Summarization
Visualization
Analytics
Applications
Filtering
Mining
Information
Organization
Information
Access
Text
Mining
Search
Extraction
Categorization
Clustering
Natural Language Content Analysis
Text
Text Acquisition

13. IR Topics (narrow view)
docs
4. Efficiency & scalability
INDEXING
Query
Rep
query
3. Document representation/structure
Doc
Rep
6. User interface (browsing)
User
Ranking
SEARCHING
1. Evaluation
2. Retrieval (Ranking) Models
results
5. Search result summarization/presentation
INTERFACE
Feedback
judgments
7. Feedback/Learning
QUERY MODIFICATION
LEARNING
Topics covered most in this course: 2, 3, 5, 7

14. Major Research Milestones
Early days (late 1950s to 1960s): foundation and founding of the field
Luhn’s work on automatic encoding
Cleverdon’s Cranfield evaluation methodology and index experiments
Salton’s early work on SMART system and experiments
1970s-1980s: a large number of retrieval models
Vector space model
Probabilistic models
1990s: further development of retrieval models and new tasks
Language models
TREC evaluation
2000s-present: more applications, especially Web search and interactions with other fields
Web search
Learning to rank
Scalability (e.g., MapReduce)
Indexing: auto vs. manual
Evaluation
System
Indexing + Search
Theory
Large-scale evaluation, beyond ad hoc retrieval
Web search
Machine learning
Scalability

15. Frontier Topics in IR: Overview
Two types of topics
30%: Fundamental challenges: IR models, evaluation, efficiency, user models/studies
70%: Application-driven challenges: Web (1.0, 2.0, 3.0?), Enterprise (text analytics), Scientific Research (bioinformatics, …)
Methodology
50%: Machine learning (feature set + supervised)
30%: Language models (unigram + unsupervised)
20%: Others (user studies, empirical experiments)
Trends
More interdisciplinary and internationalized
More diversification of topics (new applications, new methods)
Hard fundamental problems regularly revisited

16. Topics in SIGIR 2011/2012 CFP
Document Representation and Content Analysis (e.g., text representation, document structure, linguistic analysis, non-English IR, cross-lingual IR, information extraction, sentiment analysis, clustering, classification, topic models, facets)
Queries and Query Analysis (e.g., query representation, query intent, query log analysis, question answering, query suggestion, query reformulation)
Users and Interactive IR (e.g., user models, user studies, user feedback, search interface, summarization, task models, personalized search)
Retrieval Models and Ranking (e.g., IR theory, language models, probabilistic retrieval models, feature-based models, learning to rank, combining searches, diversity)
Search Engine Architectures and Scalability ( e.g., indexing, compression, MapReduce, distributed IR, P2P IR, mobile devices)
Filtering and Recommending (e.g., content-based filtering, collaborative filtering, recommender systems, profiles)
Evaluation (e.g., test collections, effectiveness measures, experimental design)
Web IR and Social Media Search (e.g., link analysis, query logs, social tagging, social network analysis, advertising and search, blog search, forum search, CQA, adversarial IR, vertical and local search)
IR and Structured Data (e.g., XML search, ranking in databases, desktop search, entity search)
Multimedia IR (e.g., Image search, video search, speech/audio search, music IR)
Other Applications (e.g., digital libraries, enterprise search, genomics IR, legal IR, patent search, text reuse)

17. My View of the Future of IR
Personalization
(User Modeling)
Large-Scale
Semantic Analysis
Full-Fledged Text
Info. Management
Access
Mining
Task Support
Search
Current Search Engine
Keyword Queries
Bag of words
Search History
Complete User Model
Entities-Relations
Knowledge
Representation

18. What You Should Know
IR is a highly interdisciplinary area interacting with many other areas, especially NLP, ML, DB, HCI, software systems, and Information Science
Major publication venues, especially ACM SIGIR, ACM CIKM, ACM TOIS, IRJ, IPM, WSDM