Abstract: An online concierge system maintains various attributes for each item. To optimize information about items displayed in an interface, the online concierge system selects a subset of attributes of an item for display based on an item category including the item. The online concierge system applies an attribute selection model to combinations of an item category and attributes associated with the item category. The attribute selection model selects one or more attributes for an item category using one or more of an engagement model trained from prior interactions by customers and an output of a large language model prompted to select relevant attributes based on the item category. When generating an interface including an item, the online concierge system includes the subset of attributes selected for an item category including the item in conjunction with the item.

Abstract: An online system may obtain an image of a receipt from a user, wherein the receipt includes a list of item identifiers and associated charges. The online system may provide a prompt to a first machine learning model including the image or extracted information from the image, and a request to provide descriptors of a list of items corresponding to the item identifiers in the image. The online system may receive from the first machine learning model as a response, the list of items and associated charges. The online system maps the list of items to one or more items in an item catalog of an online system. The online system may add the one or more items to an order associated with the user.

Abstract: A machine-learned predictive model is trained to predict potential for customer complaint. The model is part of an online concierge system. The online concierge system accesses a customer order that includes one or more items. The online concierge system determines input data for an item of the one or more items. The online concierge system determines a prediction value associated with potential for customer complaint for the item by applying the machine-learned prediction model to the input data. The online concierge system provides the prediction value to a picker client device associated with a picker who is assigned the item. The picker client device presents an alert to the picker based in part on the prediction value, and the alert includes a message that is customized to mitigate a cause of potential customer complaint for the item.

Abstract: To improve attribute prediction for items, item categories are associated with a schema that is augmented with additional attributes and/or attribute labels. Items may be organized into categories and similar categories may be related to one another, for example in a taxonomy or other organizational structure. An attribute extraction model may be trained for each category based on an initial attribute schema for the respective category and the items of that category. The extraction model trained for one category may be used to identify additional attributes and/or attribute labels for the same or another, related category.