Abstract: A centralized document system identifies content items for presentation to a user based initially on a cold-start algorithm and subsequently based on machine-learned models. The system detects a first access by the user. The system generates a user attribute vector for the user and a content vector for each content item. The system selects a first content item based on the initial cold-start algorithm and modifies a user interface to include the first content item. The system identifies an interaction with the first content item by the user. The system detects a second access by the user. The system selects a machine-learned model based on a set of interactions by the user with displayed content items. The system applies the selected machine-learned model to the set of interactions and the user attribute vector to identify a second content item and modifies the user interface to include the second content item.

Abstract: A document management system uses an objective loss function to improve the performance of optical character recognition (OCR) processes on images of documents. A user captures an image of a document, which is imported into the document management system. The document management system performs OCR on a high resolution version of the image of the document, obtaining a first set of text representative of the text of the document. The document management system applies a machine-learned model on a low-resolution version of the image of the document, producing a denoised image that is of a higher resolution than that input into the machine-learned model. The document management system performs OCR on the denoised image, obtaining a second set of text representative of the text of the document. The document management system compares the first and second sets of text in the form of an objective loss function.

Abstract: A centralized document system identifies content items for presentation to a user based initially on a cold-start algorithm and subsequently based on machine-learned models. The system detects a first access by the user. The system generates a user attribute vector for the user and a content vector for each content item. The system selects a first content item based on the initial cold-start algorithm and modifies a user interface to include the first content item. The system identifies an interaction with the first content item by the user. The system detects a second access by the user. The system selects a machine-learned model based on a set of interactions by the user with displayed content items. The system applies the selected machine-learned model to the set of interactions and the user attribute vector to identify a second content item and modifies the user interface to include the second content item.

Abstract: A centralized document system identifies content items for presentation to a user based initially on a cold-start algorithm and subsequently based on machine-learned models. The system detects a first access by the user. The system generates a user attribute vector for the user and a content vector for each content item. The system selects a first content item based on the initial cold-start algorithm and modifies a user interface to include the first content item. The system identifies an interaction with the first content item by the user. The system detects a second access by the user. The system selects a machine-learned model based on a set of interactions by the user with displayed content items. The system applies the selected machine-learned model to the set of interactions and the user attribute vector to identify a second content item and modifies the user interface to include the second content item.

Abstract: A centralized document system identifies content items for presentation to a user based initially on a cold-start algorithm and subsequently based on machine-learned models. The system detects a first access by the user. The system generates a user attribute vector for the user and a content vector for each content item. The system selects a first content item based on the initial cold-start algorithm and modifies a user interface to include the first content item. The system identifies an interaction with the first content item by the user. The system detects a second access by the user. The system selects a machine-learned model based on a set of interactions by the user with displayed content items. The system applies the selected machine-learned model to the set of interactions and the user attribute vector to identify a second content item and modifies the user interface to include the second content item.