1. A computer system for estimating quality of an interaction between a first participant and a second participant, the system comprising: a first plurality of machine learning classifiers of extended facial expressions, each classifier of the first plurality of classifiers being configured to generate a stream of first estimates of the degree to which a predetermined emotion or affective state corresponding to said each classifier of the first plurality of classifiers is present in a data stream of the first participant; a second plurality of machine learning classifiers of extended facial expressions, each classifier of the second plurality of classifiers being configured to generate a stream of second estimates of the degree to which a predetermined emotion or affective state corresponding to said each classifier of the second plurality of classifiers is present in a data stream of the second participant, wherein the first and second data streams are synchronized; a first plurality of temporal filters, each filter of the first plurality of temporal filters comprising an input connected to receive output of an associated classifier of the first plurality of classifiers, and an output; a second plurality of temporal filters, each filter of the second plurality of temporal filters comprising an input connected to receive output of an associated classifier of the second plurality of classifiers, and an output; a plurality of correlators configured to receive output signals from the first and second pluralities of temporal filters and to identify correlation patterns in the output signals of the first and second pluralities of temporal filters; and a function approximator configured to receive at least some of output signals of the plurality of correlators, the output signals of the first plurality of temporal filters, and the output signals of the second plurality of temporal filters, the function approximator being machine trained to generate one or more estimates of quality of the interaction between the first participant and the second participant based on at least some of the output signals of the plurality of correlators, the output signals of the first plurality of temporal filters, and the output signals of the second plurality of temporal filters; and a recommendation selector coupled to the function approximator to receive from the function approximator the one or more estimates and machine learning weights of the function approximator, the recommendation selector being configured to generate one or more suggestions regarding the interaction.