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公开(公告)号：US10198753B2
公开(公告)日：2019-02-05
申请号：US15157511
申请日：2016-05-18
申请人： NEC Europe Ltd.
发明人： Mathias Niepert , Lili Jiang , Mohamed Ahmed
摘要： A personalization system includes a preprocessing component configured to receive a request from a user over a communications network and generate a request key using predefined attributes of the request. A categorization component is configured to map the request key to a subset of domain-dependent vocabulary. An augmentation and buffer component is configured to augment the request with the subset of domain-dependent vocabulary mapped to the request key by the categorization component and to buffer request sequences in queues according to sequence identifiers. An embedding model component is configured to update an embedding model using the buffered request sequences. A personalization component is configured to provide a personalization using the updated embedding model.
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公开(公告)号：US20170337587A1
公开(公告)日：2017-11-23
申请号：US15157511
申请日：2016-05-18
申请人： NEC Europe Ltd.
发明人： Mathias Niepert , Lili Jiang , Mohamed Ahmed
CPC分类号： G06Q30/0271 , G06N3/0454 , G06Q10/067 , H04L67/02
摘要： A personalization system includes a preprocessing component configured to receive a request from a user over a communications network and generate a request key using predefined attributes of the request. A categorization component is configured to map the request key to a subset of domain-dependent vocabulary. An augmentation and buffer component is configured to augment the request with the subset of domain-dependent vocabulary mapped to the request key by the categorization component and to buffer request sequences in queues according to sequence identifiers. An embedding model component is configured to update an embedding model using the buffered request sequences. A personalization component is configured to provide a personalization using the updated embedding model.
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公开(公告)号：US11301774B2
公开(公告)日：2022-04-12
申请号：US15593353
申请日：2017-05-12
申请人： NEC Europe Ltd.
摘要： A method for learning latent representations of individual users in a personalization system uses a graph-based machine learning framework. A graph representation is generated based on input data in which the individual users are each represented by a node. The nodes are associated with labels. Node vector representations are learned by combining label latent representations from a vertex and neighboring nodes so as to reconstruct the label latent representation of the vertex and updating the label latent representations of the neighboring nodes using gradients resulting from application of a reconstruction loss. A classifier/regressor is trained using the node vector representations and the node vector representations are mapped to personalizations. Actions associated with the personalizations are then initiated.
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公开(公告)号：US20180247224A1
公开(公告)日：2018-08-30
申请号：US15593353
申请日：2017-05-12
申请人： NEC Europe Ltd.
摘要： A method for learning latent representations of individual users in a personalization system uses a graph-based machine learning framework. A graph representation is generated based on input data in which the individual users are each represented by a node. The nodes are associated with labels. Node vector representations are learned using message passing. A classifier/regressor is trained using the node vector representations and mapping the node vector representations are mapped to personalizations. Actions associated with the personalizations are then initiated.
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公开(公告)号：US20170228660A1
公开(公告)日：2017-08-10
申请号：US15230517
申请日：2016-08-08
申请人： NEC Europe Ltd.
CPC分类号： G06N20/00 , G06Q30/0251
摘要： A method detects an event or anomaly in real-time and triggers an action based thereon. A stream of data is received from data sources. The data includes at least two categorical features and a real-value measurement. Sketching is performed on the features using min-wise hashing to create sketches of the data. A regression tree is learnt on the sketches so as to estimate a mean squared error. It is determined whether an event or anomaly exists based on the mean squared error. An action is triggered based on at least one of a type, location or magnitude of the determined event or anomaly.
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