20. The computerized system of claim 17 , the method further comprising: receive advertisement information comprising at least one advertisement document, each document including one or more advertising terms; based on the set of weight parameters, determine a first upper bound parameter value for each of the one or more advertising terms; and store the first upper bound parameter value for each of the one or more advertising terms.
17. A computerized system comprising: one or more processors; and computer storage memory having computer-executable instructions stored thereon which, when executed by the one or more processors, implement a method comprising: receive training data for training a machine learning model, the training data comprising information indicating relevance between a set of reference and target documents, the documents comprising one or more training terms; determine one or more training data feature sets and corresponding training data feature values from the training terms of the training data, each feature set and values associated with a training term; generate, based on the training data feature sets and corresponding values, a cross-joined feature set of features comprising a combined first feature of a reference-document data feature and a second, different feature of a target-document feature; train the machine learning model by determining a set of weight parameters for each of the training terms by applying the one or more of the training feature sets, the cross-joined feature set, and values associated with the training term to the model; in response to a query, utilize the model to determine a final measure of relevance between a query-target document and the query; and based on the final measure of relevance, cause the query-target document to be presented.