System and method for generating a multi-task machine learning model

A computer-implemented method, comprising:

• 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.

A non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause at least one device to perform operations comprising:

• 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.