Princeton University
Computer Science Dept.

Computer Science 435
Information Retrieval, Discovery, and Delivery

Andrea LaPaugh

Spring 2009
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Information about the Course Project

Each student will do a final project of his or her choosing related to the material of the course.

Information on Project requirements:

Proposal due 5:00pm Thursday, Feb. 26, 2009:
Email a paragraph describing your proposed project to Prof. LaPaugh.  Include as much detail as possible.  This will be the starting point of a discussion with Professor LaPaugh to make sure the project is of the appropriate scope for a class project.


Checkpoint presentation April 16, 21, or 23:
Each student will give a 10 minute presentation on his or her project. The presentation should include the motivation and goals of the project, brief background on the topic, and progress to date.  The presentation will be given a separate grade from the final project grade.    Pairs doing a joint project should prepare one presentation during which each student speaks.  Pairs may have 15 minutes for their combined presentation.   Two to three minutes are alloted after each presentation for questions and comments.

Prepare slides to use with your presentation;  you will submit these slides after the presentation.   You may, but are not required to, see Prof. LaPaugh or Chong Wang to discuss your slides and presentation before your presentation.     Note that Professor LaPaugh is away April 13-15.

Sign up to speak on one of April 16, 21, or 23  using OIT's office hours scheduling system WASS.   Search for the (only) calendar under name "LaPaugh" or NetId "aslp", and click "Make Appointment".  At most one pairs presentation can be accommodated each day.  Blocks are 15 minutes to allow for transitions between speakers.  Pairs sign up for one block;  the extra time is accounted for in the overall schedule.

Project Report due 5:00 pm Dean's Date, Tuesday May 12, 2009: 
You are required to submit a report that describes your project. This must include the statement of the topic and the goals of the project, your methodology and the results. If it is an experimental project, you need to describe what was implemented, the major implementation decisions,  how you designed the experiments, and the experimental results. If you developed a system or tool, you may not have experiments per se, but you must describe how you are evaluating the project and the outcome.  You should also relate your work to other work on the problem.  Your code should be in an appendix or posted on a Web page with the URL provided (Web posting is preferred).   If your project is a theoretical study, you need to describe the problem, review what was known about the problem before your analysis, and give the details and the results of your theoretical analysis. If your project is a literature-based project, you need to describe the major issues under study, summarize the major techniques and the theoretical and/or experimental results presented in the literature and critically analyze the results.  For any type of project, be sure to include a bibliography of all the sources you used.

Projects will be graded on thoroughness and depth of thought. Difficulty will be taken into consideration. Keep in mind that evaluation is an important part of any project. Be clear on the goals of your project and how you demonstrate or measure success.

Project demonstration:
If you have implemented something that lends itself to live demonstration, I would like to see a final demonstration after I receive your report and before 5pm Mon. May 18, 2009.


List of suggested projects:

These topics are fairly broad and need further refinement based on a student's particular interests. Students are encouraged to suggest other project topics based on their own interests.  Check back for updates and additions.

  1. PageRank and/or HITS can be applied to any directed graph.  Explore the use of one or both of them in another application domain.  This is intended to be an experimental project, but the literature for the application should be explored as well.
  2. Investigate the use of link analysis to determine the subject of non-text pages.  For example, if a Web page contains only an image,
     not only the anchor text of links pointing to the page but the subject matter of links pointing to the page may allow one to decide the general subject of the image.  Can this be done without informative anchor text?  (An example of uninformative anchor text is here.)
  3. Investigate algorithms for stemming.  Implement one that is not too involved and study its effects on aspects of retrieval, e.g.  term frequency, document frequency and query satisfaction, using a small document collection.
  4. Investigate the use of dependence among index terms (e.g. co-occurrence) in the literature and by your own experiments.  LSI is one example of a technique that uses co-occurrence.
  5. Explore the success of doing query expansion by adding synonyms.  Web search engines will do this, and you can test results with and without expansion as part of your project.  There are also studies reported in the literature.  WordNet, a lexical database developed and maintained here at Princeton, is used in many query modification tools.
  6. Propose and implement a visualization of the relationship between some collection of objects (text documents, images, Web pages, etc.)
  7. Investigate searches for handheld display.  What special things are done now by companies  providing service?  How do search engines perform?  Are special ranking algorithms needed that do really well at getting the top few ( 5? 7?)? Are there things that can be done?  Propose one and test.
  8. Investigate probabilistic models for information retrieval. 
  9. Do a literature search and analysis of the state of the art of image retrieval by image properties, not text labels.  You should include an analysis of such retrieval systems available on the Web.  Any other non-text media can be substituted for images.   We will briefly discuss such not-text retrieval in class;  your research must be substantially more thorough. 
  10. Investigate the use of clustering in some application.  For example, can snippets be clustered in a way that is helpful for search results? 
  11. Several search engines currently use clustering in their presentation of search results.   Find out what you can about the clustering techniques used and assess their effectiveness from a user perspective.
  12. Experiment with techniques for detecting duplicate documents. 
  13. Investigate personalized or topic-directed crawling techniques and their effectiveness.
  14. Do an in-depth investigation of  cluster machine architectures for indexing and query-processing on large collections.   Find and compare state-of-the-art alternatives.  Some simulation may be a part of this project.  Recent publications should be the primary source of information on the state of the art..

last revised Mon Apr  6 13:44:20 EDT 2009.
Copyright  2008-2009 Andrea S. LaPaugh