1. A computer-implemented method comprising: generating a score for a subject and search keyword objects of target object provided by an online computer-based search directed to an object type, either by explicit or by behaviorally inferred subject responses, wherein the generated object score is based on relevancy to the subject and search keywords of the target objects provided by the online computer-based search; representing each of said subject and said objects in individualized vector form at a computer server, wherein the predicted relevancy of a target object to a search keyword object is generated by matching the search keyword object vector to the target object vector, and the predicted affinity of the subject to the target object is generated by matching the subject vector to the target object vector, further wherein said matching is calculated as the dot product between said vectors; generating search results by matching profiles of said subject and keywords with profiles in a target object catalog and ranking against said profile of said subject and keywords; and presenting said subject with top-ranking target objects, such that said top-ranking target objects are tailored to said subject; wherein the presented target objects are restricted to the object type of the online computer-based search; and wherein the computer server is further configured to generate the subject vector and the object vector by producing initial subject vectors and initial object vectors having respective initial dimensions, to determine predicted search relevance scores based on the initial subject vectors, initial object vectors, and subject search response data, and to calculate a cost function as the mean squared error between the predicted relevance scores and actual relevance scores across all said subject responses; and wherein the computer server iteratively increases the dimensions of the generated subject and object vectors and generates the values of the added dimensions of both said subject and object vectors to reduce the cost function based on the differences between the predicted relevance scores and actual relevance scores, until the cost function decreases to a predetermined value, and wherein the actual relevance scores are based on user input.