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AssigneeMicrosoft Technology Licensing, LLC
InventorsMayank Shrivastava, Sagar Goyal, Sahil Bhatnagar, Pin-Jung Chen, Pushpraj Shukla, Arko P. Mukherjee
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The disclosure herein describes a system for generating embeddings representing sequential human activity by self-supervised, deep learning models capable of being utilized by a variety of machine learning prediction models to create predictions and recommendations. An encoder-decoder is provided to create user-specific journeys, including sequenced events, based on human activity data from a plurality of tables, a customer data platform, or other sources. Events are represented by sequential feature vectors. A user-specific embedding representing user activities in relationship to activities of one or more other users is created for each user in a plurality of users. The embeddings are updated in real-time as new activity data is received. The embeddings can be fine-tuned using labeled data to customize the embeddings for a specific predictive model. The embeddings are utilized by predictive models to create product recommendations and predictions, such as customer churn, next steps in a customer journey, etc.
A system for generating embeddings representing sequential human activity using a self-supervised activity sequencing model, the system comprising:
A method for generating embeddings representing sequential human activity using a self-supervised activity sequencing model, the method comprising:
One or more computer storage devices having computer-executable instructions stored thereon for generating embeddings using a self-supervised activity sequencing model, that, upon execution by a processor, cause the processor to perform operations comprising:
Dependencies are inferred from the claim text.