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1. 发明授权

US11507604B1 Automated persona feature selection 有权
公开(公告)号：US11507604B1
公开(公告)日：2022-11-22
申请号：US16987755
申请日：2020-08-07
申请人： Quantcast Corporation
发明人： William Kennedy Browne
IPC分类号： G06F16/28 , G06F16/958
摘要： Embodiments of the invention include a system for automated persona feature selection. Soft clusters of entities are received, each entity having a history of features. Each feature has a general prevalence coefficient representing prevalence of entities having the respective feature in their history. A feature list is generated for each cluster, each feature having an in-cluster coefficient representing prevalence of entities in the cluster having the feature in their history. Features having an in-cluster coefficient that is different from that feature's general prevalence coefficient are selected. A variance across the clusters is determined for each selected feature. A discriminating feature list having high variance features is generated for each cluster. Clusters are selected for an entity by comparing the features of the entity's history to features of the discriminating feature lists of the clusters. Content is customized according to the chosen clusters and sent to the entity.

2. 发明授权

US10740359B1 Automated persona feature selection 有权
公开(公告)号：US10740359B1
公开(公告)日：2020-08-11
申请号：US15201229
申请日：2016-07-01
申请人： Quantcast Corporation
发明人： William Kennedy Browne
IPC分类号： G06F16/28 , G06F16/958
摘要： Embodiments of the invention include a system for automated persona feature selection. Soft clusters of entities are received, each entity having a history of features. Each feature has a general prevalence coefficient representing prevalence of entities having the respective feature in their history. A feature list is generated for each cluster, each feature having an in-cluster coefficient representing prevalence of entities in the cluster having the feature in their history. Features having an in-cluster coefficient that is different from that feature's general prevalence coefficient are selected. A variance across the clusters is determined for each selected feature. A discriminating feature list having high variance features is generated for each cluster. Clusters are selected for an entity by comparing the features of the entity's history to features of the discriminating feature lists of the clusters. Content is customized according to the chosen clusters and sent to the entity.

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