Abstract: An example method includes receiving by a server and from a computing device a natural language-based query, converting the natural language-based query into an embedding formatted query having a vector format, inputting the embedding formatted query into a machine-learning logic model, and determining by the machine-learning logic model a query response to the embedding formatted query that is predicted by the machine-learning logic model to be in context of beauty or cosmetic industry. The machine-learning logic model is trained using a multi-dimensional learned-embedding that includes semantically similar terms in proximity in an embedding space and the embedding space is limited to salient terms associated with the beauty or cosmetic industry. The method also includes communicating by the machine-learning logic model the query response to the computing device.

Abstract: An example method includes receiving by a server and from a computing device a natural language-based query, converting the natural language-based query into an embedding formatted query having a vector format, inputting the embedding formatted query into a machine-learning logic model, and determining by the machine-learning logic model a query response to the embedding formatted query that is predicted by the machine-learning logic model to be in context of beauty or cosmetic industry. The machine-learning logic model is trained using a multi-dimensional learned-embedding that includes semantically similar terms in proximity in an embedding space and the embedding space is limited to salient terms associated with the beauty or cosmetic industry. The method also includes communicating by the machine-learning logic model the query response to the computing device.