Abstract: Methods, computer readable media, and devices for machine learning for automated tagging of requests for e-commerce conversion funnel tracking. One method may include retrieving a plurality of requests previously grouped into a user session, generating preprocessed request information by determining a request type, a relative time, and a keyword count for each of the plurality of requests and determining a determining a relative time distribution for each of the plurality of keywords, and applying a machine learning model to the plurality of requests and the preprocessed request information to tag each of the plurality of requests as being associated with one of the plurality of conversion classifications.

Abstract: An adaptive document curation method. A machine learning model is trained on a first library of documents to assign staleness scores to documents, each indicating a confidence that the document to which it is assigned should be archived. If a document has a staleness score at least equal to a threshold, the document is identified as a candidate to be archived or deleted.

Abstract: Methods, computer readable media, and devices for machine learning for automated tagging of requests for e-commerce conversion funnel tracking. One method may include retrieving a plurality of requests previously grouped into a user session, generating preprocessed request information by determining a request type, a relative time, and a keyword count for each of the plurality of requests and determining a determining a relative time distribution for each of the plurality of keywords, and applying a machine learning model to the plurality of requests and the preprocessed request information to tag each of the plurality of requests as being associated with one of the plurality of conversion classifications.

Abstract: Methods, computer readable media, and devices for predictive product availability for grocery delivery are presented. A method may include determining a shopping location and a future delivery window. A predicted availability of one or more grocery product items may be generated based on the location and delivery window. If the predicted availability of the items exceeds a threshold, the items may be presented to a user for selection as part of an order. If the predicted availability of the items does not exceed a threshold, alternative shopping locations and/or alternative future delivery windows may be presented to the user for selection. A machine learning algorithm may be implemented to generate the predicted availability of the one or more grocery product items, the one or more alternative shopping locations, and/or the one or more future delivery windows.

Abstract: An adaptive document curation method. A machine learning model is trained on a first library of documents to assign staleness scores to documents, each indicating a confidence that the document to which it is assigned should be archived. If a document has a staleness score at least equal to a threshold, the document is identified as a candidate to be archived or deleted.

Abstract: An adaptive document curation method. A machine learning model is trained on a first library of documents to assign staleness scores to documents, each indicating a confidence that the document to which it is assigned should be archived. If a document has a staleness score at least equal to a threshold, the document is identified as a candidate to be archived or deleted.

Abstract: An adaptive document curation method. A machine learning model is trained on a first library of documents to assign staleness scores to documents, each indicating a confidence that the document to which it is assigned should be archived. If a document has a staleness score at least equal to a threshold, the document is identified as a candidate to be archived or deleted.

Abstract: A method of identifying a task to be completed in a task-tracking system. A first task to be completed by a user is used to identify a second task that can be completed by the user. A similarity score indicating a similarity of the second task to the first task can be used to identify the second task as being related to the first task.