1. A computer implemented method for automatically determining any of importance of an on-line asset and expertise that one or more members of an online community possess, without asking said community members directly identifying, extracting, capturing, and leveraging expertise and knowledge, comprising the processor executing the steps of: observing usage by a community of peers and experts who show high affinity in connection with online assets one or more documents; employing automatic techniques to extract patterns from said usage; identifying usefulness of an online asset by observing user implicit behaviors in connection with said usage patterns of said online asset and by extracting behavioral patterns from said observations; refining said identified online asset usefulness by context, wherein the context of each online asset is automatically detected based on observed terms/topics from individual and group user behaviors when said online asset is determined to be useful based upon said individual and group user behaviors; assigning to each said online asset a document impact factor score for each possible topic/term, said document impact factor representing the importance of each said online asset to each topic; assigning to each user an expert impact factor which is determined by aggregating identified topics of online assets each user has found useful, weighted by the document impact factor and by document rareness, wherein said expert impact factor and other observed patterns of behavior define a user's identified expertise; and using said identified expertise of each user to identify a community of experts given a specific topic/term of interest expressed by a user, and to identify a community of peers for a given user based upon a relationship between a target user's identified expertise and all other users; said observed usage patterns comprising user online search, navigation, and interaction behavior, said behavior including any of searches performed and position in user trail; assets viewed and position in user trail; dwell, range, scrolling, think time, and mouse movement on an asset; anchors and lines used in asset text; virtual bookmarks and virtual printing; and explicit downloading, emailing, printing, saving, and removing to or from a hardware storage device.
1. A computer implemented method for automatically determining any of importance of an on-line asset and expertise that one or more members of an online community possess, without asking said community members directly identifying, extracting, capturing, and leveraging expertise and knowledge, comprising the processor executing the steps of: observing usage by a community of peers and experts who show high affinity in connection with online assets one or more documents; employing automatic techniques to extract patterns from said usage; identifying usefulness of an online asset by observing user implicit behaviors in connection with said usage patterns of said online asset and by extracting behavioral patterns from said observations; refining said identified online asset usefulness by context, wherein the context of each online asset is automatically detected based on observed terms/topics from individual and group user behaviors when said online asset is determined to be useful based upon said individual and group user behaviors; assigning to each said online asset a document impact factor score for each possible topic/term, said document impact factor representing the importance of each said online asset to each topic; assigning to each user an expert impact factor which is determined by aggregating identified topics of online assets each user has found useful, weighted by the document impact factor and by document rareness, wherein said expert impact factor and other observed patterns of behavior define a user's identified expertise; and using said identified expertise of each user to identify a community of experts given a specific topic/term of interest expressed by a user, and to identify a community of peers for a given user based upon a relationship between a target user's identified expertise and all other users; said observed usage patterns comprising user online search, navigation, and interaction behavior, said behavior including any of searches performed and position in user trail; assets viewed and position in user trail; dwell, range, scrolling, think time, and mouse movement on an asset; anchors and lines used in asset text; virtual bookmarks and virtual printing; and explicit downloading, emailing, printing, saving, and removing to or from a hardware storage device. 2. The method of claim 1 , further comprising the step of: classifying usage of a given asset by a given user as successful or unsuccessful use based on a combination of factors comprising any of time spent on an online asset, explicit user actions taken on said asset, said actions comprising any of print, save, email, and bookmark, mouse and keyboard behavior, length and distribution of stable pauses between activity, navigation to and from said asset, and browser window state, said state comprising any of foreground, background, minimized, maximized, and closed.