1. A computer-implemented method for automatically determining within an on-line community the topic/context of each on-line asset, in which an affinity engine, comprising a processor, executes the steps of: observing usage patterns by a community associated with said asset; 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 every term and phrase a term vector entry for said asset that describes a degree to which an identified asset topic has an affinity with each said term and phrase, given the observed usage patterns: refining a rating of said term vector entry based on learned associations between assets derived from community navigation and usage patterns; using said term vector entry ratings for any of: determining and describing a topic of said asset; determining and describing inherent relationships between topics; determining and describing inherent relationships between assets; determining and describing a current interest of a user; determining and describing similarity between a user's current interest and each asset; 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 the topic/context of each on-line asset, in which an affinity engine, comprising a processor, executes the steps of: observing usage patterns by a community associated with said asset; 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 every term and phrase a term vector entry for said asset that describes a degree to which an identified asset topic has an affinity with each said term and phrase, given the observed usage patterns: refining a rating of said term vector entry based on learned associations between assets derived from community navigation and usage patterns; using said term vector entry ratings for any of: determining and describing a topic of said asset; determining and describing inherent relationships between topics; determining and describing inherent relationships between assets; determining and describing a current interest of a user; determining and describing similarity between a user's current interest and each asset; 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 step of: representing asset topics using a vector-space model and UF-IDF (usage frequency, inverse document frequency) weighting.