Automated software selection using a vector-trained deep learning model

A method comprising:

• building, by a processing device, a list of software application dependencies and corresponding metatags for each of the software application dependencies;
• generating, by the processing device, a probability distribution based on the list of software application dependencies and corresponding metatags;
• generating, by the processing device, user vectors for the software application dependencies using collaborative data for a software ecosystem corresponding to the software application dependencies;
• sampling, by the processing device, the probability distribution to produce a latent vector space wherein distances between representative vectors from the latent vector space indicate similarity;
• and
• combining, by the processing device, using matrix factorization, the latent vector space and the user vectors to train a hybrid deep learning model to provide software application dependency recommendations.

A non-transitory computer-readable medium comprising program code that is executable by a processing device for causing the processing device to:

• build a list of software application dependencies and corresponding metatags for each of the software application dependencies;
• generate a probability distribution based on the list of software application dependencies and corresponding metatags;
• generate user vectors for the software application dependencies using collaborative data for a software ecosystem corresponding to the software application dependencies;
• sample the probability distribution to produce a latent vector space wherein distances between representative vectors from the latent vector space indicate similarity;
• and
• combine, using matrix factorization, the latent vector space and the user vectors to train a hybrid deep learning model to provide software application dependency recommendations.