Understanding Web Searching: Feedback, Representation, and Browsing Behavior

1. Understanding Web Searching Secondary Readings and So On… Will Meurer for WIRED October 7, 2004

2. Introduction • Why do we care about how people use the Web? • Today’s topics (10/7, not the present age): • Implicit vs. explicit feedback • Representation effectiveness • Browser-based activities • History mechanisms • How do we cater to the people? • Resources • Research

3. Implicit vs. Explicit FeedbackReading Time, Scrolling and… (Kelly & Belkin, 2001) • Implicit feedback (Morita & Shinoda): • Time spent on a page is directly related to user interest. Backed by many studies. • Explicit feedback (this study) • Time spent on a page is similar for relevant and irrelevant content. • Results suggest: • “Generalizability” is severely affected by explicit feedback methods. • Spend time to choose the right feedback type!

4. Implicit vs. Explicit FeedbackReading Time, Scrolling and… (Kelly & Belkin, 2001) • Why do the results differ? • Relevance was difficult to distinguish this time • Participants are truly interested in the content former studies • Users may have rushed to complete in this experimental context

5. Representation Effectiveness How we really use the Web (Krug, 2000) Three “facts of life”: • “We don’t read pages. We scan them.” • Why? hurry, necessity, habit • If we are to read its entirety, we save or print! (ClearType project)

6. Representation Effectiveness How we really use the Web (Krug, 2000) • “We don’t make optimal choices. We Satisfice.” • Why? hurry, quick access to and fro, less work than thinking • Generally, it’s more productive to guess.

7. Representation EffectivenessHow we really use the Web (Krug, 2000) • “We don’t figure out how things work.” • Why? not important, “if it ain’t broke (baroque)…” • Is it important to us whether the user understands how it works or not? Why?

8. Representation EffectivenessCognitive Strategies in Web… (Navarro-Prieto, et al, 1999) • Users get lost on the Web. Why? • It is not just interactivity between user and system, rather user, task, and information • Analysis structure of browsing behavior presented and tested “The Interactivity Framework” or “How we should analyze cognitive strategies”

9. Representation EffectivenessCognitive Strategies in Web… (Navarro-Prieto, et al, 1999) • The Interactivity Framework • User Level – Web experience, cognitive processes, cognitive style, knowledge (CS majors knew more about SE processes) • User Strategies – based on searching structure (or lack of), task nature

10. Representation EffectivenessCognitive Strategies in Web… (Navarro-Prieto, et al, 1999) • Information Structure • Internal (user’s) representation • External (system’s) representation • Computational Offloading – How much work does the user have to do to understand and how much does a representation help? • Re-representation – How much it makes problem solving easier or more difficult • Graphical Constraining – How it constrains inferences • Temporal and Spatial Constraining – How it helps when distributed over time and space

11. Representation EffectivenessCognitive Strategies in Web… (Navarro-Prieto, et al, 1999)

12. Representation EffectivenessCognitive Strategies in Web… (Navarro-Prieto, et al, 1999) • More Results • Experienced users searched with a plan • By having a plan you keep a more internal representation and focus your search • Inexperienced users were more influenced by external representations • Computational Offloading Results • Must explain • How have these issues changed?

13. Representation Effectiveness Cognitive Strategies in Web… (Navarro-Prieto, et al, 1999) • Conclusions • Cognitive strategies used by the participants depend on how the information is structured. • Interaction is a multi-dimensioned concept. • Search engine interfaces should be designed to have less restrictive external representation.

14. Browser-based ActivitiesCharacterizing Browsing… (Catledge & Pitkow, 1995) • User study of browsing events at the Georgia Tech (xMosaic browser) • Three main browsing strategies identified: • Search browsing – directed search, goal known • General purpose browsing – consulting highly likely sources for needed information (dictionary.com) • Serendipitous browsing – random • Most people use a combination of these

15. Browser-based ActivitiesCharacterizing Browsing… (Catledge & Pitkow, 1995) • Results • Users were patient 99% of the time for long page loads • 1222 unique sites accessed outside of GATech (~16% of Web servers) • Paths were calculated (sequences of page navigation) • Per session, paths of 7 different sites occurred 5 times • Per user, paths of 8 different sites occurred 9 times

16. Browser-based ActivitiesCharacterizing Browsing… (Catledge & Pitkow, 1995) • More Results • 2% of the retrieved pages were saved or printed • Based on user’s slope, browsing strategy categories were applied • Slope can also categorize usage patterns of Web documents • Users tended to operate in one small area of a site

17. Browser-based Activities Characterizing Browsing… (Catledge & Pitkow, 1995) • Design Strategies • Users averaged 10 pages per server • Make most important info within 2 or 3 jumps from the index • Do not put too many links on one page – increases search time (back, forward, back, site map, etc.) • Facilitate the likely visitor browser patterns • Maybe make more than one version of your page? • Most work well in a “hub and spoke” environment • The Future • Offer site tour based on most frequently traveled paths • Alter page design dynamically based on site trends

18. History Mechanisms (in browsers)Revisitation Patterns in… (Tauscher & Greenberg, 1997) • Purpose: Provide empirical data to aid in the development of effective history mechanisms • Understand revisitation patterns • Evaluate current mechanisms and suggest best practices and methods • Data Collection • Altered version of xMosaic to record activity • Survey of users afterward

19. History Mechanisms (in browsers)Revisitation Patterns in… (Tauscher & Greenberg, 1997) • Revisitation Results • 58% recurrence rate (>40% are new pages!) • As people search they build their vocabulary • 7 browsing strategies • First-time visits to cluster of pages • Revisits to pages • Authoring of pages (high reload percentage) • Regular use of web-based apps • Hub-and-spoke (breadth-first approach) • Guided tour (e.g. next page links) • Depth-first search (following links deeply before returning to the index)

20. History Mechanisms (in browsers)Revisitation Patterns in… (Tauscher & Greenberg, 1997) • Revisitation Results • Visit frequency as a function of distance • Users mostly revisit recently visited pages (within about 6 jumps) • 39% chance that the next URL will match one of the previous 6 pages visited • Access frequency • 60% of pages visited only once • 19% visited twice • 8% visited 3 times • 4% visited 4 times • Locality (not valuable for predicting next page) • Most locality sets were small • Only 2.5 to 4.5 URLs per set • Only 15% of pages were part of a locality set • Paths (not valuable for predicting next page) • Could these be captured and offered in a history mechanism? • Time per page could indicate path

21. History Mechanisms (in browsers)Revisitation Patterns in… (Tauscher & Greenberg, 1997) • Mechanism types • Recency Ordered • Sequential order based on time accessed • Repeated entries for revisitation • “Pruned” by keeping only first instance or only last • Simple for users to understand (they remember paths) • Frequency Ordered • Most visited at top, least visited at bottom • User interest changes, latest URLs must have frequency • How to break ties – last visited, earliest visited • When few items are on the list, this suffers • Difficult for users to understand

22. History Mechanisms (in browsers)Revisitation Patterns in… (Tauscher & Greenberg, 1997) • Stack-based • Recently visited at top • Order and availability depend on: • Loading – causes page to be added to the top • Recalling – changes pointer to the currently displayed page • Revisiting – user reloads the page, has no effect on the stack • Keeps duplicates • Non-persistent vs. persistent (btw sessions) • Better than recency at short distances • Users have difficulty understanding this model

23. History Mechanisms (in browsers)Revisitation Patterns in… (Tauscher & Greenberg, 1997) • Hierarchically Structured • Recency ordered hyperlink sublists • Like recency w/ latest position saved • Each URL has its own sublist of links from that page • Helps with common linking paths • Easier to understand • Context-sensitive web subspace • Somewhat of a combination of the above-mentioned and stack-based approaches • Gives user better understanding of context of his/her searches • May be difficult to remember where a certain URL was • I THINK this approach would be a great tool

24. History Mechanisms (in browsers)Revisitation Patterns in… (Tauscher & Greenberg, 1997) • Do users actually use history mechanisms? • Less than 1% of navigation • 3% involve favorites • 30% of navigation was back button usage

25. How do we cater to the people? • Inter-site browsing strategies are not easy to tackle. How would you control that? • Why should we attempt to understand user behavior and search strategies? • Formulate general design principles (e.g. 3 level depth) • Design for multiple searching personalities • Understand how to survey your intended users or get feedback most appropriately • Identify importance of all aspects of the development process and allocate resources accordingly

26. How do we cater to the people? Some Bright Ideas • Personalized search • Learning systems – You might also like… • www.a9.com (history, favorites, personalized interface) • But what about changing for different types of user behavior based on the user’s path history on your server? • Researched since 1995 and earlier! • What has resulted? • Microsoft ASP.net 2.0 – Web Parts

27. What resources are out there? • xMosaic 2.6 download, for those of you so excited • Architecture of the World Wide Web http://www.w3.org/TR/webarch/ • Sum Sun Sug Gestions http://www.sun.com/980713/webwriting/ • Jakob Nielsen – research on content usability, http://useit.com/alertbox/9710a.html

28. Research • Vox Populi: The Public Searching Of The Web (2001) • Compares statistics from two studies • Shows how public searching changed from 1997 to 1999 • Usage Patterns of a Web-Based Library Catalog (2001), Michael D. Cooper • Real Life, Real Users, and Real Needs: A Study and Analysis of User Queries on the Web (2000), Jansen, Spink & Saracevic • Redefining the Browser History in Hypertext Terms (), Mark Ollerenshaw