Methods and systems for determining a query response using a computing platform in a domain of beauty or cosmetic industry

A system comprising:

• one or more processors and non-transitory computer-readable media having stored therein instructions, which when executed by the one or more processors, causes the system to perform functions comprising:
• receiving, 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;
• 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, wherein the machine-learning logic model is trained using a multi-dimensional learned-embedding that includes semantically similar terms in proximity in an embedding space, wherein the embedding space is limited to salient terms associated with the beauty or cosmetic industry, wherein determining, by the machine-learning logic model, the query response to the embedding formatted query comprises:
• determining, by a first machine-learning logic model, a first predicted intent to the embedding formatted query, wherein the first predicted intent is predicted by the first machine-learning logic model to be in context of beauty or cosmetic industry, wherein the first machine-learning logic model is trained using data limited to salient terms associated with the beauty or cosmetic industry;
• determining, by a second machine-learning logic model, a second predicted intent to the embedding formatted query, wherein the second predicted intent is different than the first predicted intent, wherein the second predicted intent is predicted using the second machine-learning logic model that is trained using data not limited to salient terms associated with the beauty or cosmetic industry;
• combining the first predicted intent and the second predicted intent into a feature vector;
• and
• processing, by the machine-learning logic model, the feature vector to determine the query response;
• and
• communicating, by the machine-learning logic model, the query response to the computing device.

A non-transitory computer-readable media having stored therein executable instructions, which when executed by a system one or more processors causes the system to perform functions comprising:

• receiving, 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;
• 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, wherein the machine-learning logic model is trained using a multi-dimensional learned-embedding that includes semantically similar terms in proximity in an embedding space, wherein the embedding space is limited to salient terms associated with the beauty or cosmetic industry, wherein determining, by the machine-learning logic model, the query response to the embedding formatted query comprises:
• determining, by a first machine-learning logic model, a first predicted intent to the embedding formatted query, wherein the first predicted intent is predicted by the first machine-learning logic model to be in context of beauty or cosmetic industry, wherein the first machine-learning logic model is trained using data limited to salient terms associated with the beauty or cosmetic industry;
• determining, by a second machine-learning logic model, a second predicted intent to the embedding formatted query, wherein the second predicted intent is different than the first predicted intent, wherein the second predicted intent is predicted using the second machine-learning logic model that is trained using data not limited to salient terms associated with the beauty or cosmetic industry;
• combining the first predicted intent and the second predicted intent into a feature vector;
• and
• processing, by the machine-learning logic model, the feature vector to determine the query response;
• and
• communicating, by the machine-learning logic model, the query response to the computing device.