This is the report of the W3C Uncertainty Reasoning for the World Wide Web Incubator Group (URW3-XG) as specified in the Deliverables section of its charter.
In this report we present requirements for better defining the challenge of reasoning with and representing uncertain information available through the World Wide Web and related WWW technologies.
Specifically the report:
* identifies and describes situations on the scale of the World Wide Web for which uncertainty reasoning would significantly increase the potential for extracting useful information,
* identifies methodologies that can be applied to these situations and the fundamentals of a standardized representation that could serve as the basis for information exchange necessary for these methodologies to be effectively used,
* includes a set of use cases illustrating conditions under which uncertainty reasoning is important,
* provides an overview and discusses the applicability to the World Wide Web of prominent uncertainty reasoning techniques and the information that needs to be represented for effective uncertainty reasoning to be possible,
* includes a bibliography of work relevant to the challenge of developing standardized representations for uncertainty and exploiting them in Web-based services and applications.
The report identifies various areas which require further investigation and debate.
"Here's a preliminary data mining analysis of musical social networking service Last.fm. An automated classification into clusters or sub populations with related musical genres reveals the structure of musical preferences among the users in a relatively large sample population. Musical tag clouds are adopted to characterise users and populations, which adds a highly descriptive value and aids with the interpretation of the results."
Fang Wu and Bernardo A. Huberman
HP Laboratories
Palo Alto, CA 94304
January 23, 2008
Abstract
We analyze the role that popularity and novelty play in attracting
the attention of users to dynamic websites. We do so by determining
the performance of three different strategies that can be utilized to
maximize attention. The first one prioritizes novelty while the second
emphasizes popularity. A third strategy looks myopically into
the future and prioritizes stories that are expected to generate the
most clicks within the next few minutes. We show that the first two
strategies should be selected on the basis of the rate of novelty decay,
while the third strategy performs sub-optimally in most cases. We also
demonstrate that the relative performance of the first two strategies
as a function of the rate of novelty decay changes abruptly around a
critical value, resembling a phase transition in the physical world.