Microsoft Technology Licensing, LLC

Divya Venugopalan

Shaunak Chatterjee

Hongche Liu

Aastha Jain

Parag Agrawal

Ashish Jain

Techniques for tackling delayed user response by modifying training data for machine-learned models are provided. In one technique, a first machine-learned model generates a score based on a set of feature values. A training instance is generated based on the set of feature values. An attribute of the training instance is modified based on the score to generate a modified training instance. The attribute may be an importance weight of the training instance or a label of the training instance. The modified training instance is added to a training data. One or more machine learning techniques are used to train a second machine-learned model based on the training data.

Tackling delayed user response by modifying training data for machine-learned models

A machine for improving content delivery generates a graph representing a personalized conversational flow for sequenced delivery of digital content. The graph includes nodes representing interactive dialogues between a machine and a user, and edges that connect the nodes. The machine causes display of a user interface including a prompt related to job-seeking guidance. The machine, based on a first action in response to the prompt, dynamically adjusts the graph, the dynamic adjusting including selecting a first node. The machine generates and causes display of a first incentive content item, and a first call-to-action content item. The machine, in response to a second action received in response to the first call-to action content item, dynamically selects an edge connecting the first node and a further node. The dynamic selecting of the edge results in display of a further incentive content item, and a further call-to-action content item.

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Redwood City, CA, United States of America

Divya Venugopalan