1. A computer-implemented method for automatically determining within an on-line community any of importance of an on-line asset, topic of said asset, and relationships among assets, without asking members of said on-line community directly, comprising the processor executed steps of: observing usage patterns by a community of peers and experts who show high affinity to a topic related to a navigation context; 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; determining a user's current context/topic based on an aggregation of the identified context of each online asset that the said user has found useful during their navigation, where an asset's contribution to the aggregate context is weighted by an asset's recency of use; refining said identified user context based on searches performed during the user's navigation, where a search's contribution to the aggregate context is weighted by the recency of the search; comparing the identified context of the current user with the identified context of all assets within the system and assigning to each asset a similarity score based on its similarity to the current user's context; using said similarity score to predict a desired destination of users in said navigation context; and refining said predicted destination based on observed navigation patterns of previous 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 and/or from a computer hardware memory.
1. A computer-implemented method for automatically determining within an on-line community any of importance of an on-line asset, topic of said asset, and relationships among assets, without asking members of said on-line community directly, comprising the processor executed steps of: observing usage patterns by a community of peers and experts who show high affinity to a topic related to a navigation context; 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; determining a user's current context/topic based on an aggregation of the identified context of each online asset that the said user has found useful during their navigation, where an asset's contribution to the aggregate context is weighted by an asset's recency of use; refining said identified user context based on searches performed during the user's navigation, where a search's contribution to the aggregate context is weighted by the recency of the search; comparing the identified context of the current user with the identified context of all assets within the system and assigning to each asset a similarity score based on its similarity to the current user's context; using said similarity score to predict a desired destination of users in said navigation context; and refining said predicted destination based on observed navigation patterns of previous 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 and/or from a computer hardware memory. 3. The method of claim 1 , further comprising the steps of: differentiating navigation information on a one per-asset basis; and basing predictions on a combination of information from all points in a user's session navigation history.