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Augmenting (personal) IR

Augmenting (personal) IR. Readings Review Evaluation Papers returned & discussed Papers and Projects checkin time. Relevance Feedback in IR. Already in most systems Improved query formulations System evaluation of system Works from natural characteristics in documents

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Augmenting (personal) IR

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  1. Augmenting (personal) IR Readings Review Evaluation Papers returned & discussed Papers and Projects checkin time

  2. Relevance Feedback in IR • Already in most systems • Improved query formulations • System evaluation of system • Works from natural characteristics in documents • More interesting to work from the NC of people • Personal Relevance Feedback • If you don’t know what the document set is, how do you reformulate a query? • Browse by query, then search • (Bibliometric) chaining

  3. Old School Query Reformulation • Identify core terms in document database • Deemphasize (not index?) less core terms • “We know what’s good for you” • Small set of documents • Accurate knowledge of users • Small steps, building to quality documents • Weights of queries are shifted • Preferred terms • Partial weights 0 - 1

  4. It’s all about vectors • Remember VectorSpace? • Documents • Queries • How similar is the query to a document? • Averages and weights give final set • Length and location

  5. Probability Relevance Feedback • Document-based not term based • Ranking documents be their content • And tweaked weights • Depends on variety of documents in database • Wider variance = harder to predict • More processing power can help • Means to average and normalize values • Ad hoc adjustment, relative weighting • Using the found documents as additional queries • How do you evaluate RFS as doc db changes? • Previous retrieval is key, but not with changes • Adding common terms may help (in general)

  6. IR & Filtering • Are they the same? • Is a filter a proactive search? • Does filtering lead to better browsing, which leads to less need for searching? • Good for lots of changing text (Web) • Active use • What about push media with filters? • RSS • Email

  7. What do we mean by augment? • Douglas Englebart’s system • GUI • Interaction • Connectivity • Management • Improve upon • Extend user capabilities • Do what you want, but faster • “Do what I mean, not what I say” • What are some ways to augment?

  8. What is Personalization? • In computing? • Optimized • System specific • In interfaces? • Modes of interaction • Appropriate for user level • For IR? • Results • Time • Mode • (Relevance) Feedback

  9. Personalized IR system design • How would you design a personal IR system? • Who would use it? • How would you learn about them? • Interests • Sources • Preferences • How do you evaluate a personal system? • Understanding users is the key to personalizing search or search interfaces.

  10. Letizia • Interleaving browsing with (automated) search • Augmented browsing = less searching? • Understanding your usage preferences • “Behavior based” • Letizia explores for you • “doing concurrent, autonomous exploration of links from the user’s current position” p1 • PageRank for individuals? • PageRank for the exact situation? • Smart crawling based on a profile?

  11. Letizia’s Inferences • What you do tells the systems your interests and habits • List of keywords about your interests • Persistence of interest issues • Shifts • Time to restate interest • Automated queries, keyword matches • Doesn’t get in the way (much) • What about the interface? • Making Web search better?

  12. Siteseer • “Personalized navigation for the Web” • Isn’t this a CF system? • Bookmarks are key indicators of interest • Category fits • Implicit recommendations

  13. How to personalize the Web: WBI • Interests are bookmarks or home pages • Links • Text • Proxy-like between the user the Web • Agent like functions • Monitor - records features • Editor - tweaks retrieved information • Generator - request to response • Autonomous agent - triggers

  14. Outride • Data mining for personalized search • Fast model fitting for profiles • Search keyword augmentation • Interests • Preferences • “Contextual Computing” • Just in time information • Situational • More than content analysis • “Author relevancy”

  15. Personalized Search Efficiency • Contextualization • Activity • Availability • Individualization • User goals (models of Iseek) • (Past) behavior • Interface • Awareness and customization

  16. Personalization vs. Customization • What’s the difference? • For a system, for a user • Interaction methods, selection methods • My.yahoo.com vs. amazon.com • AskJeeves vs. a Reference Librarian

  17. WIRED System Evaluations • Install IR software • Set up documents for indexing • What types of documents • Sizes, formats, time to index? • Perform some searches • Note search functionality • Describe (screen shot?) interface for search • Examine results • Describe (screen shot?) results page/screen • Rotate, use subset of documents • Note differences in queries • What model, index, system do you think the system uses (based on class discussions & readings)?

  18. System Evaluation Questions • What do these systems seem to offer? • How would you use them? • How would a group use them? • Can it affect the way you search? • The way you work? • The way you store/organize information? • What’s different than you expected? • Better or Worse? • From your deign ideas?

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