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User-Centric Web Search: We-Centric Aspect

User-Centric Web Search: We-Centric Aspect. Roman Y. Shtykh Waseda University, Japan. Information Explosion. We had created 5EB of information in 2002. 1EB (exabytes) = 10 18 , or 1GB multiplied a billion times

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User-Centric Web Search: We-Centric Aspect

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  1. User-Centric Web Search:We-Centric Aspect Roman Y. Shtykh Waseda University, Japan

  2. Information Explosion • We had created 5EB of information in 2002. • 1EB (exabytes) = 1018, or 1GB multiplied a billion times • This is 500,000 times the total information contained in the U.S. Library of Congress. • Moreover, the information added in the following two years (2003 and 2004) was greater than the total amount of information created since the beginning of history. http://www.igvpj.jp/e/about_project/index.html

  3. Burden on a User • About 30% time of intellectual work spent on retrieval Kitsuregawa, IEEE APSCC2007 Keynote, Dec. 13, 2007

  4. Information Overload is Subjective • Miller considered human cognitive capacity to be limited to five to nine “chunks” of information. Miller, G. A. (1956), “The magical number seven, plus or minus two: some limits on our capacity for processing information”, Psychological Review, Vol. 63, pp. 81-97. • “… the feeling of stress when the information load goes beyond the processing capacity” Mulder, I., de Poot, H., Verwij, C., Janssen, R. and Bijlsma, M. (2006) “An information overload study: using design methods for understanding”, Proceedings of the 2006 Australasian Computer-Human Interaction Conference (OZCHI 2006), pp. 245-252. Solution to overcome it must be highly human-centric.

  5. I-Centric Web Search • Personalisation (or adaptation) through intelligent interfaces, search algorithms, etc. • Oriented mainly on an individual’s search experience implies both user-adaptive and adaptable systems

  6. Personalisation Definitions • “a process that changes the functionality, interface, information content, or distinctiveness of a system to increase its personal relevance to an individual” Blom, J., Monk, A., 2003. Theory of personalisation of appearance: why people personalise their mobile phones and PCs. Human–Computer Interaction. “a process that changes the functionality, interface, information access and content, or distinctiveness of a system to increase its personal relevance to an individual or a category of individuals” Fan, H., & Poole, M. S. (2006). What is personalization? Perspectives on the design and implementation of personalization in information systems. Journal of Organizational Computing and E-Commerce, 16, 179-202.

  7. We-Centric Web Search (1) Problem: I-Centric approach produces different experiences, gives different answers, and, as a result, reduces common understanding and increases the chances of problems related to such reduction. Solution: Incorporating community knowledge will diminish the problems.

  8. We-Centric Web Search (2) Problem: “Cold start” problem, bias, innacuracy Solution: I-Centric approach can be balanced with community-generated knowledge to enhance the predictive power of individual-oriented personalisation.

  9. We-Centric Web Search (3) Problem: Explicit relevance feedback gives an accurate understanding of user’s contexts, but hard to collect Solution: New types of relevance feedback – “explicit motivated” -> increase in the accuracy of personalisation.

  10. Combining I- & We-Centric Web Approaches Community-oriented Web search, more than vertical search engines I-Centric Web Search We-Centric Web Search

  11. BESS Contribution through search User modelling using contributions Search result ranking based on the user’s individual and community expertise Contribution assessment based on the user’s expertise inferred from user models (both individual and collective)

  12. BESS

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