Human-Centered Approaches to Data Mining: Where does Culture meet Statistics?

Transcripción

1 Human-Centered Approaches to Data Mining: Where does Culture meet Statistics? Manuel Cebrián & Alejandro (Alex) Jaimes ICT 2008 Collective Intelligence Networking Nov. 26, 2008 Leading PROGRESS

2 Outline Trends Data Mining and User Modeling Patterns Challenges and opportunities

3 Trend I (Technical) Increased connectivity, data centers Integrated devices and services

4 Trend II (behavoiral) Strong reliance on search On-Off line convergence, real time

5 Trend III (Socio-economic) Spread of technology Flat world

6 We have More data than at any time in history (more than can be stored) Better tools to store it, access it, process it Better (sometimes) technology for our day to day Larger diversity of users

7 And where are we? Overly complex systems & information overload Culturally un-balanced tools & content (content gap) Leaving out more than half of the world s population

8 Data Mining & User Modeling Human-Centered Computing (HCC) Social-cultural Psychological Economic Machine Learning, Context, User Modeling, Personalization (cultural analytics, science)

9 Culture Patterns of human activity and the symbolic structures that give such activities significance and importance. Systems of symbols and meanings Lack fixed boundaries Constantly changing Interact and compete with one another [Findley et. al. 06] And, in Information Systems, its a set of algorithms!

10 Patterns Contextual patterns Behavioural patterns Socio-cultural patterns

11 Contextual Patterns - Tokyo

12 Contextual Patterns - Bombay

13 Behavioral Patterns

14 Socio-Cultural Patterns

15 Socio-cultural Patterns: structure?

16 Socio-cultural Patterns: on-line vs. offline?

17 User Modeling (Hofstede 97)

18 Collective vs. Individual (Hofstede 97)

19 We need to use the knowledge to be more Proactive Anticipate user desires (but not too much) Trustable & reliable Sensitive (mood, privacy) Simple & efficient Eliminate unnecessary choices Eliminate repetitiveness Intelligent Point the non-obvious (find new connections)

20 Challenges Selecting relevant factors (socio-cultural, psychological, economic) Mapping to variables (non-binary) Personal vs. cultural factors? Models & reasoning (quantitative, qualitative)

21 Opportunities Increased connectivity, data centers, integrated devices and services Real time (system, infrasctructure-strategic) Strong reliance on search, on-off line convergence, real time Location & context-based (user models) Spread of technology, flat world Micro-trade (informal, not just consumers)

22 Research Areas Dynamic, real-time user modeling (stream mining) Cultural ontology Social modeling (influence, data intensive) Cultural science (analytics) [J. Hartley, L. Manovich] Human-centered Data Mining (interactive, informed) Hybrid modeling Culture-specificity is good for business, for users, and essential for development

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