1. Evaluating IR (Web) Systems Study of Information Seeking & IR Pragmatics of IR experimentation The dynamic Web Cataloging & understanding Web docs Web site characteristics

2. Study of Info seeking & retrieval - Well known authors (useful for research papers) Real life studies (not TREC) - User context of questions - Questions (structure & classification) - Searcher (cognitive traits & decision making) - Information Items Difference searches with same question Relevant items “models, measures, methods, procedures and statistical analyses” p 175 Beyond common sense and anecdotes

3. Study 2 Is there ever enough user research? A good set of elements to include in an IR system evaluation How do you test for real life situations? - Questions the users actually have - Expertise in subject (or not) - Intent - User’s computers, desks & materials What’s a search strategy? - Tactics, habits, previous knowledge How do you collect search data?

4. Study 3 How do you ask questions? - General knowledge test - Specific search terms Learning Style Inventory - NOT the best way to understand users - Better than nothing - Choose your questions like your users Let users choose their questions? Let users work together on searches Effectiveness Measures - Recall, precision, relevance

5. Study 4 Measuring efficiency - Time on tasks - Task completion Correct answer Any answer? - Worthwhile? Counting correct answers Statistics - Clicks, commands, pages, results - Not just computer time, but the overall process - Start with the basics, then get advanced - Regression analysis (dependencies for large studies)

6. Let’s design an experiment User Selection - Searcher (cognitive traits & decision making) - User context of questions Environment Questions (structure & classification) Information Items - Successful answers - Successful/Worthwhile sessions Measurement

7. Pragmatics of IR experimentation The entire IR evaluation must be planned Controls are essential Working with what you can get - Expert defined questions & answers - Specific systems Fast, cheap, informal tests - Not always, but could be pre-tests - Quick results for broad findings

8. Pragmatic Decision1 Testing at all? - Purpose of test - Pull data from previous tests Repeat old test - Old test with new system - Old test with new database Same test, many users - Same system - Same questions (data)

9. Pragmatic Decision 2 What kind of test? Everything at once? - System (help, no help?) - Users (types of) - Questions (open-ended?) Facts - Answers with numbers - Words the user knows General knowledge - Found more easily - Ambiguity goes both ways

10. Pragmatic Decision 3 Understanding the Data What are your variables? (p 207) Working with initial goals of study Study size determines measurement methods - Lots of user - Many questions - All system features, competing system features What is acceptable/passable performance? - Time, correct answers, clicks? - Which are controlled?

11. Pragmatic Decision 4 What database? - The Web (no control) - Smaller dataset (useful to user?) Very similar questions, small dataset - Web site search vs. whole Web search - Prior knowledge of subject - Comprehensive survey of possible results beforehand Differences other than content?

12. Pragmatic Decision 5 Where do queries/questions come from? - Content itself - User pre-interview (pre-tests) - Other studies What are search terms (used or given) - Single terms - Advanced searching - Results quantity

13. Pragmatic Decisions 6, 7, 8 Analyzing queries - Scoring system - Logging use What’s a winning query (treatment of units) - User success, expert answer - Time, performance - Different querie with same answer? Collect the data - Logging and asking users - Consistency (software, questionnaires, scripts)

14. Pragmatic Decisions 9 & 10 Analyzing Data Dependent on the dataset Compare to other studies Basic statistics first Presenting Results Work from plan Purpose Measurement Models Users Matching other studies

15. Keeping Up with the Changing Web Building Indices is difficult enough in theory What about a continuously changing huge volume of information? Is old information good? What does up-to-date mean anymore? Is Knowledge a depreciating commodity? - Correctness + Value over time Different information changes at different rates - Really it’s new information How do you update an index with constantly changing information?

16. Changing Web Properties Known distributions for information change Sites and pages may have easily identifiable patterns of update - 4% change on every observation - Some don’t ever change (links too) If you check and a page hasn’t changed, what is the probability it will ever change? Rate of change is related to rate of attention - Machines vs. Users - Measures can be compared along with information

17. Dynamic Maint. of Indexes w/Landmarks Web Crawlers do the work in gathering pages Incremental crawling means incremented indices - Rebuild the whole index more frequently - Devise a scheme for updates (and deletions) - Use supplementary indices (i.e. date) New documents Changed documents 404 documents

18. Landmarks for Indexing Difference-based method Documents that don’t change are landmarks - Relative addressing - Clarke: block-based - Glimpse: chunking Only update pointers to pages Tags and document properties are landmarked Broader pointers mean less updates Faster indexing – Faster access?

19. Yahoo! Cataloging the Web How do information professionals build an “index” of the Web? Cataloging applies to the Web Indexing with synonyms Browsing indexes vs searching them Comprehensive index not the goal - Quality - Information Density Yahoo’s own ontology – points to site for full info Subject Trees with aliases (@) to other locations “More like this” comparisons as checksums

20. Yahoo uses tools for indexing

21. Investigation of Documents from the WWW What properties do Web documents have? What structure and formats do Web documents use? What properties do Web documents have? - Size – 4K avg. - Tags – ratio and popular tags - MIME types (file extensions) - URL properties and formats - Links – internal and external - Graphics - Readability

22. WWW Documents Investigation How do you collect data like this? - Web Crawler URL identifier, link follower - Index-like processing Markup parser, keyword identifier Domain name translation (and caching) How do these facts help with indexing? Have general characteristics changed? (This would be a great project to update.)

23. Properties of Highly-Rated Web Sites What about whole Web sites? What is a Web site? - Sub-sites? - Specific contextual, subject-based parts of a Web site? - Links from other Web pages: on the site and off - Web site navigation effects Will experts (like Yahoo catalogers) like a site?

24. Properties Links & formatting Graphics – one, but not too many Text formatting – 9 pt. with normal style Page (layout) formatting – min. colors Page performance (size and acess) Site architecture (pages, nav elements) - More links within and external - Interactive (search boxes, menus) Consistency within a site is key How would a user or index builder make use of these?

25. Extra Discussion Little Words, Big Difference - The difference that makes a difference - Singular and plural noun identification can change indices and retrieval results - Language use differences Decay and Failures - Dead links - Types of errors - Huge amount of dead links (PageRank effective) 28% in 1995-1999 Computer & CACM 41% in 2002 articles Better than the average Web page?

26. Break!

27. Topic Discussions Set Leading WIRED Topic Discussions - About 20 minutes reviewing issues from the week’s readings Key ideas from the readings Questions you have about the readings Concepts from readings to expand on - PowerPoint slides - Handouts - Extra readings (at least a few days before class) – send to wired listserv

28. Web IR Evaluation - 5 page written evaluation of a Web IR System - technology overview (how it works) Not an eval of a standard search engine Only main determinable diff is content - a brief overview of the development of this type of system (why it works better) - intended uses for the system (who, when, why) - (your) examples or case studies of the system in use and its overall effectiveness

29. How can (Web) IR be better? - Better IR models - Better User Interfaces More to find vs. easier to find Web documents sampling Web cataloging work - Metadata & IR - Who watches the catalogers? Scriptable applications - Using existing IR systems in new ways - RSS & IR Projects and/or Papers Overview

30. Project Ideas Searchable Personal Digital Library Browser hacks for searching Mozilla keeps all the pages you surf so you can search through them later - Mozilla hack - Local search engines Keeping track of searches Monitoring searches

31. Paper Ideas New datasets for IR Search on the Desktop – issues, previous research and ideas Collaborative searching – advantages and potential, but what about privacy? Collaborative Filtering literature review Open source and IR systems history & discussion