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?