Application display and discovery by predicting behavior through machine-learning

A method for ordering software applications in a computing environment, comprising:

• presenting user-selectable icons for launching a plurality of software applications in a graphical user interface of a first computing device in accordance with a first order;
• selecting, by the first computing device or a second remote computing device, at least one group from a plurality of groups in which a first user is a member based on first weighting values respectively assigned to the groups, wherein each said group has a list of software applications associated therewith which is different than lists of software applications associated with other ones of the plurality of groups;
• performing, by the first computing device or the second remote computing device, at least one machine-learning algorithm to determine a second weighting value for each software application of the plurality of software applications contained in the list associated with the at least one group which was selected from the plurality of groups, based on at least one of a first criteria specifying at least one aspect of a software launch request, a second criteria specifying a popularity of the software application, and a third criteria that is different than the second criteria and that specifies a learned behavior regarding software application usage of a plurality of people;
• performing, by the first computing device or the second remote computing device, at least a second machine-learning algorithm to determine a third weighting value based on a similarity of the first user to one or more second users among the plurality of people, the similarity based on a plurality of factors including the use of software applications, geographic location, organizational positions within a structure of an organization, and social media activities;
• assigning, by the first computing device or the second remote computing device, the plurality of software applications to a plurality of different categories based on the first, second and third weighting values;
• and
• dynamically modifying the graphical user interface to present the user-selectable icons in accordance with category assignments made during said assigning.