2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 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

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 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, Challenge-Impact Analysis Level of Challenges Impact/Usefulness Known Unknown Good applications Not interesting for research High impact Low risk (easy) Good short-term research problems High impact High risk (hard) Good long-term research problems Difficult basic research Problems, but questionable impact Low impact Low risk Bad research problems (May not be publishable) Your research proposal

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 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, 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, 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, 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) – (public vs. personal ) –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, Map of IR Applications Web pages News articles messages Literature Organization docs Legal docs/Patents Medical records Customer complaint letter/transcripts … Kids Peking Univ. community LawyersScientists SearchBrowsingAlertMining Task/Decision support Customer Service People management + automatic reply “Google Kids” Legal Info Systems Literature Assistant Intranet Search Local Web Service Blog articles Online Shoppers ?

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 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, 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, 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 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, 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, 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, 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, Next Lecture : Formulate IR Research Hypothese