1. Human Memory Model Predicting Document Access in Large Multimedia Repositories (1996) JAMES E. PITKOW, MARGARET M. RECKER Sam Boham, Asif Hussaini, Christian Lorenz, Ed Watson

2. Content 1. Introduction 2. Paper 2.1. Model 2.2. Repository 2.3. Analysis 2.3.1. Frequency Analysis/Results 2.3.2. Recency Analysis/Results 2.3.3. Combining Frequency and Recency 2.4. Applications 2.5. Conclusions 3. Future Work 3.1 By Authors 3.2 By Other People 4. Summary

3. 1. Introduction “We may look into that window on the mind as though a glass darkly, but what we are beginning to discern there looks very much like a reflection of the world“ [Shepard 1990, p. 213]

4. 1. Introduction 2. Paper 2.1. Model 2.2. Repository 2.3. Analysis 2.3.1. Frequency Analysis/Results 2.3.2. Recency Analysis/Results 2.3.3. Combining Frequency and Recency 2.4. Applications 2.5. Conclusions 3. Future Work 3.1 By Authors 3.2 By Other People 4. Summary

5. The Model Study of human memory with long tradition (psych. literature) Review of memory literature by Anderson and Schooler Relationship R1: Practice trials  subsequent performance during test Known as power law of practice E.g.: 10 light bulbs, push button with corresponding finger Robust relationship (in motor-perceptual and cognitive tasks) Relationship R2 (from related memory research): Time delay in representation  subsequent performance on recall

6. The Model (2) R1 and R2: Approximation as power function Form of a power function: P = A * T^(-b) P is the measure of performance T represents time A and b as parameters of the model Obtaining linear relationship: logP = logA – b * logT Figure on next slide [Schooler and Anderson 1991, pp. 397]

7. The Model (3) [Anderson 1981, p. 10]

8. The Model (4) New approach (Anderson and Schooler): Environmental explanation for these relationships (R1/R2) Memory system adapted to the structure of the environment Memory system: Make memory available that are most likely to be needed Need probability (p) Probability that a particular item will be needed at the present moment Most items not needed / few only frequently Need Odds: New distribution ==> "NEED ODDS“ = p/(1-p)

9. The Model (5) p  [0,1] p/(1-p)  [0,+  ] log(p/(1-p))  [- ,+  ] Algorithm that analyses the occurrence of items in large repositories –Predict future access –Applied to analysis of repositories of information in terms of: Frequency Recency Spacing rates (not observed) Used repositories: Newspaper headlines of the New York Times Utterances made to children Email-adresses from mails sent to one person

10. The Repository The Georgia Tech WWW repository is a dynamic information ecology Over 2000 multimedia documents Fluctuations in document access Monthly updated data Document deletions, insertions, renamings However, fundamental characteristics of a dynamic information ecology  Need to develop methods for prediction and information- seeking patterns

11. 1. Introduction 2. Paper 2.1. Model 2.2. Repository 2.3. Analysis 2.3.1. Frequency Analysis/Results 2.3.2. Recency Analysis/Results 2.3.3. Combining Frequency and Recency 2.4. Applications 2.5. Conclusions 3. Future Work 3.1 By Authors 3.2 By Other People 4. Summary

12. 2.3.1. Frequency Analysis/Results [Recker and Pitkow 1996, pp.358]

13. 2.3.1. Frequency Analysis/Results (2) 72% [Recker and Pitkow 1996, pp. 361]

14. 2.3.2. Recency Analysis/Results [Recker and Pitkow 1996, p.358]

15. 2.3.2. Recency Analysis/Results (2) 92% [Recker and Pitkow 1996, pp.363]

16. 2.3.3. Combining Recency and Frequency 97% [Recker and Pitkow 1996, pp.366]

17. 1. Introduction 2. Paper 2.1. Model 2.2. Repository 2.3. Analysis 2.3.1. Frequency Analysis/Results 2.3.2. Recency Analysis/Results 2.3.3. Combining Frequency and Recency 2.4. Applications 2.5. Conclusions 3. Future Work 3.1 By Authors 3.2 By Other People 4. Summary

18. Applications Use on Non-Text Information to increase the relevance. Design of Information System –When dynamics pages are involved Navigation Strategies –Designing of websites Visualization of Access

19. Applications (2) Caching Algorithms –Removes the needs for heuristics [Recker and Pitkow 1996, p.371]

20. Conclusion Good for caching of systems where there is a there a lot of user with few requests. This model seems to have reached it limits in terms of progress, it doesn’t seem to be expanded on For over 100 frequency accesses there is increased variability in the prediction of probability of access. These effects have to be dismissed and so the model loses strength Choice of the window and the pane non-empirically

21. 1. Introduction 2. Paper 2.1. Model 2.2. Repository 2.3. Analysis 2.3.1. Frequency Analysis/Results 2.3.2. Recency Analysis/Results 2.3.3. Combining Frequency and Recency 2.4. Applications 2.5. Conclusions 3. Future Work 3.1 By Authors 3.2 By Other People 4. Summary

22. James Pitkow Using this model to help clustering of web pages –Life, Death, and Lawfulness on the Electronic Frontier (1997) Included it as part of the overall picture of relating documents together Explains how the desirability of information changes with time Not much further after (1999) [Pitkow and Pirolli 1997, p.387]

23. James Pitkow (2) Looked at the Problem, using different Models Strong Regularities in World Wide Web Surfing(1998) –Looking at Page Hits –Using Real Data – Xerox, Aol, etc –Modelling using Gaussian distribution Visualisations to view the problem Visualizing the Evolution of Web Ecologies(1998) Emerging Trends in the WWW User Population(1996) [Chi, Pitkow, Mackinlay, Pirolli, Gossweiler and Card (1998)]

24. 1. Introduction 2. Paper 2.1. Model 2.2. Repository 2.3. Analysis 2.3.1. Frequency Analysis/Results 2.3.2. Recency Analysis/Results 2.3.3. Combining Frequency and Recency 2.4. Applications 2.5. Conclusions 3. Future Work 3.1 By Authors 3.2 By Other People 4. Summary

25. Future Work WebViz: A Tool for WWW Access Log Analysis –A Tool for database designers and maintainers giving them a graphical display of the data. –Tool establishes an access pattern. –The tool helps structural and contextual changes resulting in more efficient use of the document space.

26. Future Work (2) “Stuff I’ve Seen” - A System for Personal Information Retrieval and Re-use –Assumes: Most knowledge work involves finding and re-using previously used information –The system provides a unified index of information that a person has seen before –Uses rich contextual clues –Users found information more easily when using “Stuff I’ve Seen”

27. Future Work (3) Characterizing Reference Locality in the WWW (1996) Presents a New Model for characterizing web access patterns for engineering web caching systems. Based on Work by Piktow and Recker Combines both Spatial information and temporal Information: –Spatial Locality - data stored close together –Temporal Locality – property that data likely to be accessed soon again after being recently accessed

28. 1. Introduction 2. Paper 2.1. Model 2.2. Repository 2.3. Analysis 2.3.1. Frequency Analysis/Results 2.3.2. Recency Analysis/Results 2.3.3. Combining Frequency and Recency 2.4. Applications 2.5. Conclusions 3. Future Work 3.1 By Authors 3.2. By Other People 4. Summary

29. Summary Well tested model. Accurately predicts future use. Wide range of applications. Been taken further but in a limited way.

30. References Shepard, R. N. (1990). Mind sights. New York: Freeman. Schooler, L. J. and Anderson, J. R. (1991). Reflections of the Environment in Memory. Anderson, J. R. (1981). Cognitive Skills and Their Acquisition. Recker, M. M. and Pitkow, J. E. (1996). Predicting Document Acess in Large Multimedia Repositories. Pitkow, J. and Pirolli, P. (1997). Life, Death, and Lawfulness on the Electronic Frontier. Conference on Human Factors in Computing Systems, CHI '97, Atlanta Chi, E. H., Pitkow, J., Mackinlay, J., Pirolli, P., Gossweiler, R. and Card, S. K. (1998). Visualizing the Evolution of Web Ecologies. ACM Conference on Human Factors in Software (CHI '98), Los Angeles 400-407