Automatic routing of user inquiries using machine-learning models

A non-transitory computer readable storage medium having instructions encoded thereon that, when executed by one or more processors of a computing system, cause the one or more processors to perform steps comprising:

• receiving an inquiry from a client device associated with a user, the inquiry comprising text content and an image;
• applying one or more machine learning models to analyze the text content and the image to identify a first category of items specified in the text content and a second category of items contained in the images;
• identifying a retailer that carries items in at least one of the first or second category of items by applying an item availability model to the first or second category of items of a plurality of retailers, wherein the item availability model is trained using historical inventory records from the plurality of retailers;
• in response to identifying the retailer that carries the items in the at least one of the first or second category of items, sending, to the client device associated with the user, suggestion information that causes the client device associated with the user to display one or more user interfaces that suggest the retailer to the user;
• identifying a shopper associated with the retailer who is a subject matter expert in the first or second category of items based on an expertise score of the shopper, wherein the expertise score of the shopper is determined by applying a shopper expertise model trained using data associated with historical orders completed by the shopper, the shopper expertise model is trained by
• accessing data about a plurality of shoppers labeled with expertise scores, the data about each of the plurality of shoppers including data about historical orders completed by the shopper, the historical orders including items in the first or second category;
• applying parameters of the shopper expertise model to the data about each of the plurality of shoppers to predict an expertise score;
• and
• in response to the predicted expertise score differing from the labeled expertise score, adjusting the parameters of the shopper expertise model to reduce the difference between the labeled expertise score and the predicted expertise score;
• and
• in response to identifying the shopper associated with the retailer who is a subject matter expert of the first or second category of items, transmitting the user's inquiry to the shopper's client device for display, thereby enabling direct communication between the user and the shopper.

A computing system comprising:

• one or more processors;
• and
• a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by one or more processors, cause the one or more processors to perform steps comprising:
• receiving an inquiry from a client device associated with a user, the inquiry comprising text content and an image;
• applying one or more machine learning models to analyze the text content and the image to identify a first category of items specified in the text content and a second category of items contained in the images;
• identifying a retailer that carries items in at least one of the first or second category of items by applying an item availability model to the first or second category of items of a plurality of retailers, wherein the item availability model is trained using historical inventory records from the plurality of retailers;
• in response to identifying the retailer that carries the items in the at least one of the first or second category of items, sending, to the client device associated with the user, suggestion information that causes the client device associated with the user to display one or more user interfaces that suggest the retailer to the user;
• identifying a shopper associated with the retailer who is a subject matter expert in the first or second category of items based on an expertise score of the shopper, wherein the expertise score of the shopper is determined by applying a shopper expertise model trained using data associated with historical orders completed by the shopper, the shopper expertise model is trained by
• accessing data about a plurality of shoppers labeled with expertise scores, the data about each of the plurality of shoppers including data about historical orders completed by the shopper, the historical orders including items in the first or second category;
• applying parameters of the shopper expertise model to the data about each of the plurality of shoppers to predict an expertise score;
• and
• in response to the predicted expertise score differing from the labeled expertise score, adjusting the parameters of the shopper expertise model to reduce the difference between the labeled expertise score and the predicted expertise score;
• and
• in response to identifying the shopper associated with the retailer who is a subject matter expert of the first or second category of items, transmitting the user's inquiry to the shopper's client device for display, thereby enabling direct communication between the user and the shopper.