Abstract: A processing device can establish a machine learning model to produce software dependency recommendations. The model can be periodically retrained to update its knowledge of available dependencies. The software dependencies can be incorporated into software by developers who receive the selection or automatically by an intelligent software development platform. A processing device can train the model by assembling sparse user data based on feedback corresponding to software dependencies to produce a vector of preferences for each user. The processing device can also generate a latent vector of attributes for each software dependency. The processing device can then apply matrix factorization to the vectors to produce a behavior matrix that is used to train the machine learning model.

Abstract: A processing device can establish a machine learning model to produce software dependency recommendations. The model can be periodically retrained to update its knowledge of available dependencies. The software dependencies can be incorporated into software by developers who receive the selection or automatically by an intelligent software development platform. A processing device can train the model by assembling sparse user data based on feedback corresponding to software dependencies to produce a vector of preferences for each user. The processing device can also generate a latent vector of attributes for each software dependency. The processing device can then apply matrix factorization to the vectors to produce a behavior matrix that is used to train the machine learning model.