AUTOMATED SOFTWARE SELECTION USING A VECTOR-TRAINED DEEP LEARNING MODEL

摘要

A processing device can establish a vector-trained, deep learning model to produce software dependency recommendations. The processing device can build a list of software dependencies and corresponding metatags for each of the software dependencies, and generate a probability distribution from the list. The processing device can sample the probability distribution to produce a latent vector space that includes representative vectors for the software dependencies. The processing device can train a hybrid deep learning model to produce software dependency recommendations using the latent vector space as well as collaborative data for the software dependencies.