pick a good ir research problem
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Pick a Good IR Research Problem. ChengXiang Zhai Department of Computer Science Graduate School of Library & Information Science Institute for Genomic Biology, Statistics University of Illinois, Urbana-Champaign http://www-faculty.cs.uiuc.edu/~czhai, [email protected]. - PowerPoint PPT PresentationTRANSCRIPT
2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 1
Pick a Good IR Research Problem
ChengXiang ZhaiDepartment of Computer Science
Graduate School of Library & Information Science
Institute for Genomic Biology, Statistics
University of Illinois, Urbana-Champaign
http://www-faculty.cs.uiuc.edu/~czhai, [email protected]
2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 2
What is a Good Research Problem?
• Well-defined: Would we be able to tell whether we’ve solved the problem?
• Highly important: Who would care about the solution to the problem? What would happen if we don’t solve the problem?
• Solvable: Is there any clue about how to solve it? Do you have a baseline approach? Do you have the needed resources?
• Matching your strength: Are you at a good position to solve the problem?
2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 3
Challenge-Impact Analysis
Level of Challenges
Impact/Usefulness
Known
UnknownGood applications
Not interestingfor research
High impactLow risk (easy)
Good short-termresearch problems
High impactHigh risk (hard)Good long-term
research problemsDifficult
basic researchProblems,
but questionable impact
Low impactLow risk
Bad research problems(May not be publishable)
Your research proposal
2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 4
How to Find a Problem?
• Application-driven (Find a nail, then make a hammer)
– Identify a need by people/users that cannot be satisfied well currently (“complaints” about current data/information management systems?)
– How difficult is it to solve the problem?
• No big technical challenges: do a startup
• Lots of big challenges: write a research proposal
– Identify one technical challenge as your topic
– Formulate/frame the problem appropriately so that you can solve it
• Aim at a completely new application/function (find a high-stake nail)
2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 5
How to Find a Problem? (cont.) • Tool-driven (Hold a hammer, and look for a nail)
– Choose your favorite state-of-the-art tools • Ideally, you have a “secret weapon”
• Otherwise, bring tools from area X to area Y
– Look around for possible applications
– Find a novel application that seems to match your tools
– How difficult is it to use your tools to solve the problem? • No big technical challenges: do a startup
• Lots of big challenges: write a research proposal
– Identify one technical challenge as your topic
– Formulate/frame the problem appropriately so that you can solve it
• Aim at important extension of the tool (find an unexpected application and use the best hammer)
2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 6
How to Find a Problem? (cont.)
• In practice, you do both in various kinds of ways
– You talk to people in application domains and identify new “nails”
– You take courses and read books to acquire new “hammers”
– You check out related areas for both new “nails” and new “hammers”
– You read visionary papers and the “future work” sections of research papers, and then take a problem from there
– …
2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 7
Three Basic Questions to Ask about an IR Problem
• Who are the users?– Everyone vs. Small group of people
• What data do we have?– Web (whole web vs. sub-web)
– Email (public email vs. personal email)
– Literature (general vs. special discipline)
– Blog, forum, …
• What functions do we want to support?– Information access vs. knowledge acquisition
– Decision and task support
Everyone (who has an Internet connection)
The whole web (indexed by Google)
Search (by keywords)
2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 8
Map of IR Applications
Web pages
News articles
Email messages
Literature
Organization docs
Legal docs/Patents
Medical records
Customer complaint letter/transcripts
…
KidsPeking Univ. community
Lawyers Scientists
Search Browsing Alert MiningTask/Decision
support
CustomerServicePeople
Email management+ automatic reply
“Google Kids”
Legal InfoSystems
LiteratureAssistant
IntranetSearch
LocalWeb
Service
Blog articles
OnlineShoppers
?
2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 9
High-Level Challenges in IR
• How to make use of imperfect IR techniques to do something useful?
– Save human labor (e.g., partially automate a task)
– Create “add on” value (e.g., literature alert)
– A lot of HCI issues (e.g., allowing users to control)
• How to develop robust, effective, and efficient methods for a particular application?
– Methods need to “work all the time” without failure
– Methods need to be accurate enough to be useful
– Methods need to be efficient enough to be useful
2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 10
Challenge 1: From Search to Information Access
• Search is only one way to access information
• Browsing and recommendation are two other ways
• How can we effectively combine these three ways to provided integrated information access?
• E.g., artificially linking search results with additional hyperlinks, “literature pop-ups”…
2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 11
Challenge 2: From Information Access to Task Support
• The purpose of accessing information is often to perform some tasks
• How can we go beyond information access to support a user at the task level?
• E.g., automatic/semi-automatic email reply for customer service, literature information service for paper writing (suggest relevant citations, term definitions, etc), comparing prices for shoppers
2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 12
Challenge 3: Support Whole Life Cycle of Information
• A life cycle of information consists of “creation”, “storage”, “transformation”, “consumption”, “recycling”, etc
• Most existing applications support one stage (e.g., search supports “consumption”)
• How can we support the whole life cycle in an integrated way?
• E.g., Community publication/subscription service (no need for crawling, user profiling)
2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 13
Challenge 4: Collaborative Information Management
• Users (especially similar users) often have similar information need
• Users who have explored the information space can share their experiences with other users
• How to exploit the collective expertise of users and allow users to help each other?
• E.g., allowing “information annotation” on the Web (“footprints”), collaborative filtering/retrieval,
2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 14
Optimizing “Research Return”:Pick a Problem Best for You
Your Passion
High (Potential)
Impact
Your Strength
Best problems for you
Find your passion: If you don’t have to work/study for money, what would you do?
Test of impact: If you are given $1M to fund a research project, what would you fund?
Find your strength: If you don’t know your strength, at least avoid your weakness; acquire strength through training
2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 15
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