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公开(公告)号：US20240346533A1
公开(公告)日：2024-10-17
申请号：US18740485
申请日：2024-06-11
发明人： Mayank SHRIVASTAVA , Sagar GOYAL , Sahil BHATNAGAR , Pin-Jung CHEN , Pushpraj SHUKLA , Arko P. MUKHERJEE
IPC分类号： G06Q30/0202 , G06N3/04 , G06N3/084 , G06N20/00
CPC分类号： G06Q30/0202 , G06N3/04 , G06N3/084 , G06N20/00
摘要： 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.
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公开(公告)号：US20210365965A1
公开(公告)日：2021-11-25
申请号：US16930279
申请日：2020-07-15
发明人： Mayank SHRIVASTAVA , Sagar GOYAL , Sahil BHATNAGAR , Pin-Jung CHEN , Pushpraj SHUKLA , Arko P. MUKHERJEE
摘要： 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.
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