Abstract: Systems and methods for generating a machine learning model to support multiple tasks for semantic retrieval, embedding and classification are disclosed. In some embodiments, a disclosed method includes: obtaining a training data set generated based on search related data and item related data associated with a website; training, based on the training data set, a machine learning model using a two-tower structure to generate an optimized set of model parameters, wherein the optimized set of model parameters minimizes a total loss function computed based on at least one of: a query classification loss, an item classification loss, and a similarity loss; and transmitting the trained machine learning model to be utilized for at least one of the following tasks: semantic item retrieval, query classification, item classification, query clustering, and item clustering.

Abstract: Systems and methods for generating a machine learning model to support multiple tasks for semantic retrieval, embedding and classification are disclosed. In some embodiments, a disclosed method includes: obtaining a training data set generated based on search related data and item related data associated with a website; training, based on the training data set, a machine learning model using a two-tower structure to generate an optimized set of model parameters, wherein the optimized set of model parameters minimizes a total loss function computed based on at least one of: a query classification loss, an item classification loss, and a similarity loss; and transmitting the trained machine learning model to be utilized for at least one of the following tasks: semantic item retrieval, query classification, item classification, query clustering, and item clustering.