Information Foraging & Information Scent: Theory, Models, and Applications

1. Information Foraging & Information Scent:Theory, Models, and Applications Peter Pirolli User Interface Research Work supported in part by the Office of Naval Research

2. Aim of this Talk • Overview • Information foraging theory • Information scent • Sample of psychological investigations • Sample of applications

3. Human-Information Interaction:Can approach from user or producer side Server Data base Web pages

4. Overview • Motivations, origins, assumptions • Initial development: Scatter/Gather use • Extension: WIF-ACT model of WWW use • Information scent as a critical parameter of the large-scale shape of WWW use

5. Motivations & origins • Humans are informavores (George Miller, 1983) • Organisms that hunger for information about the world and themselves • Humans seek, gather, share, and consume information in order to adapt

6. Growth of available information 1000000 100000 Journals 10000 1000 100 10 1 Journals/People x106 0.1 0.01 1750 1800 1850 1900 1950 2000 Year Pressures of the information environment Source: Price (1963)

7. Growth in attention 1000000 100000 10000 1000 100 Capacity of human working memory 10 1 0.1 0.01 1750 1800 1850 1900 1950 2000 Year Pressures of the information environment

8. Pressures of the information environment “ A wealth of informationcreates a poverty of attention and a need to allocate it efficiently ” Herbert A. Simon

9. WWW challenges HCI theory • 2003 e-commerce revenue = $1 Trillion (est.) BUT • 65% of virtual shopping trips end in failure (Souza, 2000) • 1M site visitors, 40% do not return, cost=$2.8 M (Manning, 1998) • WWW site redesigns = $1.5 M/yr to $2.8 M/yr (Manning, 1998)

10. Information Foraging Theory • Take concept of informavores seriously • Key ideas • Information scent. Local cues used to explore and search information spaces • Economics of attention and the cost structure of information • Optimal foraging models

11. Take concept of informavores seriously • Information processing systems evolve so as to maximize the gain of valuable information per unit cost • Sensory systems (vision, hearing) • Information access (card catalogs, offices) • Natural selection has made animals (and our human ancestors) very good at searching for food (foraging) • Modern information foragers use problem-solving abilities with deep evolutionary roots in food foraging ] [ information value cost of interaction maximize

12. Problem solving • Decision making 10-1000 • Visual search • Motor behavior 1-100 • Visual attention • Perceptual judgment Pete Pirolli's Home Page Peter Pirolli. ... Palo Alto, CA 94304 USA phone: +1-650-812-4483 fax: +1-650-812-4241 email: pirolli@parc.xerox.com This page updated December 18, 2000. www.parc.xerox.com/istl/members/pirolli/pirolli.html - 9k - Cached - Similar pages .100-1 Time scales of analysis Psychological domain User Interface Domain Time scale (s)

13. Example: Scatter/Gather • Information scent • Optimal foraging analyses • ACT-IF cognitive model • Evaluation by user simulation

14. Example: Scatter/Gather • Information scent • Optimal foraging analyses • ACT-IF cognitive model • Evaluation by user simulation

15. information scent Tokyo Cues that facilitate orientation, navigation, assessment of information value New York San Francisco

16. Scatter/Gather • supports exploration/browsing of very large full-text collections (~ 1,000,000) • creates clusters of content-related documents • presents users with overviews of cluster contents • allows user to navigate through clusters and overviews • More recently extended to multi-modal Scatter/Gather (Chen et al., 1999) • Images + text

18. Scatter/Gather task Display Titles Window Scatter/Gather Window Law Nat. Lang. World News Robots AI Expert Sys CS Planning Medicine Bayes. Nets

19. 7 Observed Rating 6 Predicted Rating 5 Probability relevant 4 3 2 1 0 1 2 3 4 5 6 7 8 9 10 Rank information scent new cell Information Need medical patient Text snippet • Spreading activation • Derived from models of human memory • Activation reflects likelihood of relevance given past history and current context • Approximates Bayesian network treatments dose procedures beam

20. Activation of node i Ai = Bi + WjSji Base-level activation Activation spread from linked nodes j Pr(i) Pr(not i) Bi = ln( ) i “bread” j “butter” Pr(j|i) Pr(j|not i) Sji = ln( ) spreading activation Base-level reflects likelihood of occurrence Strength of link spread reflects likelihood of cooccurrance

21. spreading activation networks(for modeling “scent”) Document corpus Word statistics Spreading activation network

22. interface provides good scent of underlying document clustering Perceived by model Identified by computer

23. Summary: Information Scent • Spreading activation predicts user judgments • Networks built a priori. Only need to estimate one scaling parameter from user data • Can be used to assess “goodness of links”

24. Example: Scatter/Gather • Information scent • Optimal foraging analyses • ACT-IF cognitive model • Evaluation by user simulation

25. cost/value estimates • TREC • queries and expert-identified relevant documents • Analysis of clustering algorithm • distribution of relevant information over clusters • Time costs

26. Number of relevant documents in cluster Time to process cluster  = Activation from cluster text a a t1 + N t2  = Time to process relevant docs Time to process all docs RSG = RD at t + 1 total relevant documents task time RD = foraging evaluations choose cluster enrich exploit

27. Number of relevant documents in cluster Time to process cluster  = Optimum Total relevant documents Total time R = R Choose clusters (in descending rank p) if p > R Cluster selection (optimal diet model) 16 14  12 10 Relevant documents/second 8 6 4 2 0 0 1 2 3 4 5 6 7 8 9 10 Rank profitability

28. enrichment v exploitation relevant documents time cost R= .06 R*SG > R*D R*D > R*SG .05 if user chooses to display clusters now .04 R*D R*SG if user chooses to display later (after more Scatter/Gather) Rate of gain .03 .02 .01 0 0 200 400 600 800 1000 Time (sec)

29. Example: Scatter/Gather • Information scent • Optimal foraging analyses • ACT-IF cognitive model • Evaluation by user simulation

30. Model-Tracing Method Trace Cognitive Model System Psychology Users Optimal foraging theory

31. ACT-IF production system Procedural Memory Declarative Memory Perceptual Input Condition -> Action Foraging evaluation heuristics Condition -> Action Condition -> Action Motor Output Condition -> Action

32. production rule evaluations SELECT-RELEVANT-CLUSTER Goal is to Process Scatter/Gather Window & there is a Query & there is an unselected cluster Select the cluster DO-SCATTER/GATHER Goal is to Process Scatter/Gather Window & there is a Query & some clusters have been selected Scatter/Gather the window DO-DISPLAY-TITLES Goal is to Process Scatter/Gather Window & there is a Query & some clusters have been selected Scatter/Gather the window p RSG RD

33. Model predicts user action 250 200 150 Frequency 100 50 0 1 2 3 4 5 6 7 8 9 10 More Rank of Predicted Production

34. Example: Scatter/Gather • Information scent • Optimal foraging analyses • ACT-IF cognitive model • Evaluation by user simulation

35. Evaluation by user simulation 50 50 Faster Interaction 40 40 Improved Clustering Improved Clustering 30 30 Percent Change from Baseline (Relevant Documents) Faster 20 20 Interaction 10 10 0 0 Few Many Soft Hard Repository Results Relevant to Task Task Deadline Condition

36. Summary: Scatter/Gather • ACT-IF model matches user behavior • (most of) Model specified a priori • People optimize value/cost using foraging heuristics

37. Overview • Motivations, origins, assumptions • Initial development: Scatter/Gather use • Extension: WIF-ACT model of WWW use • Information scent as a critical parameter of the large-scale shape of WWW use

38. WIF-ACT • Web Information Foraging - ACT • Not a reality yet • Preliminary version interacts with Internet Explorer • What we have done: • Specialized instrumentation • Methodology • Preliminary analysis of information foraging and information scent

39. Cached pages Cached pages WebLogger WebLogger WebEyeMapper WebEyeMapper Event log Event log Points of regard Points of regard Eye tracker Eye tracker Interface objects Interface objects Database & statistics Database & statistics Fixation table Fixation table Visualizations Visualizations Instrumentation

40. WebLogger Event File

44. Study • 6 “Find information” tasks, e.g., • “You are Chair of Comedic events for Louisiana State University in Baton Rouge. Your computer has crashed and you have lost several advertisements for upcoming events. You know that the Second City tour is coming to your theatre in the spring, but you do not know the precise date. Find the date the comedy troupe is playing on your campus. Also find a photograph of the group to put on the advertisement.” • 12 Stanford University students • 2 tasks (CITY, ANTZ) analyzed for 4 participants

45. Analysis • Task/Information environment • Information patch structure • Problem space structure • Information scent

46. Yahoo Movie Posters Archive 123 Posters Information structure • Web sites • Portals • Search engines • Pages • Website home page • Search engine page • Hitlist page • Content elements

47. TU TU www.antzthemovie.com www.google.com CL CL www.antz.com www.antz.com/antzstore S 123 Posters Antz Problem space structure • URL • Link • Keyword • Visual Search

48. Web Behavior Graph

49. Web Behavior Graph State in Problem Space Hit List

50. Web Behavior Graph Execution of Operator Return to Previous State