1. A computer-implemented method comprising, in one or more processors, executing the steps of: provisioning a server coupled to a network for communications; indexing demographic data defined at multiple levels of granularity; indexing a body of knowledge corpus of encyclopedic text; indexing a knowledge corpus of encyclopedic text article categories and their interconnections to define a category space comprising a set of categories; storing data representing the content of the text articles as an inverted index in a datastore accessible to the server; storing data representing the category space as a graph in a datastore accessible to the server; storing data representing the indexed demographic data in a data store accessible to the server; acquiring individual location data associated with each one of a plurality of remote users over the communications network; acquiring user text data associated with each one of the plurality of remote users over the communications network; acquiring psychographic data associated with each one of the plurality of remote users over the communications network; projecting the location data associated with each one of the plurality of remote users onto the demographic data store to infer their demographic data; projecting the user text data associated with each one of the plurality of remote users onto the encyclopedic data store to form a content vector space defined by the acquired text data; projecting the user text data on to the defined category space to infer each user's characteristics as a corresponding n-dimensional concept vector based on the said category space, each dimension of the vector having a value for including, or conversely excluding, the corresponding category; receiving object data at the server associated with an object for matching the object to at least one of the remote users, wherein the object data includes at least one of demographic data, psychographic data, and textual description associated with the object; projecting the object data on to the defined vector, demographic and category spaces, including representing the object as an n-dimensional concept vector; determining a first similarity metric for each user relative to the object, based on the corresponding demographic data; estimating a second similarity metric for each user relative to the object, based on the corresponding n-dimensional text vectors; matching the object to at least one of the users based on a weighted combination of the first and second similarity metrics.
1. A computer-implemented method comprising, in one or more processors, executing the steps of: provisioning a server coupled to a network for communications; indexing demographic data defined at multiple levels of granularity; indexing a body of knowledge corpus of encyclopedic text; indexing a knowledge corpus of encyclopedic text article categories and their interconnections to define a category space comprising a set of categories; storing data representing the content of the text articles as an inverted index in a datastore accessible to the server; storing data representing the category space as a graph in a datastore accessible to the server; storing data representing the indexed demographic data in a data store accessible to the server; acquiring individual location data associated with each one of a plurality of remote users over the communications network; acquiring user text data associated with each one of the plurality of remote users over the communications network; acquiring psychographic data associated with each one of the plurality of remote users over the communications network; projecting the location data associated with each one of the plurality of remote users onto the demographic data store to infer their demographic data; projecting the user text data associated with each one of the plurality of remote users onto the encyclopedic data store to form a content vector space defined by the acquired text data; projecting the user text data on to the defined category space to infer each user's characteristics as a corresponding n-dimensional concept vector based on the said category space, each dimension of the vector having a value for including, or conversely excluding, the corresponding category; receiving object data at the server associated with an object for matching the object to at least one of the remote users, wherein the object data includes at least one of demographic data, psychographic data, and textual description associated with the object; projecting the object data on to the defined vector, demographic and category spaces, including representing the object as an n-dimensional concept vector; determining a first similarity metric for each user relative to the object, based on the corresponding demographic data; estimating a second similarity metric for each user relative to the object, based on the corresponding n-dimensional text vectors; matching the object to at least one of the users based on a weighted combination of the first and second similarity metrics. 5. The method of claim 1 wherein said estimating the second similarity metric in the case of a user who has not revealed psychographic dimension comprises: extracting the title of the object the user has some affinity over matching the object data to one of the text articles of the encyclopedic text corpus; retrieving the category of the matching article; and representing the category of the matching article in the expanded n-dimensional category space; and matching object with n-dimensional category representation to other objects with n-dimensional category representation based on pairwise similarity.