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公开(公告)号：US20210125098A1
公开(公告)日：2021-04-29
申请号：US16666728
申请日：2019-10-29
发明人： Michael Peran , Josh Price , Daniel Augenstern , Rahul Nahar , Pankaj Srivastava
IPC分类号： G06N20/00 , G06F16/9535 , G06Q10/06 , G06Q30/06 , G06N5/04
摘要： Embodiments of the present disclosure include a computer-implemented method and system for determining when to retrain an individual-item model within a recommendation engine. The computer-implemented method includes defining a consumer feature vector having attributes of historical consumers that impact an individual-item model. The computer-implemented method further includes calculating a historical feature vector relating to the historical consumers. The computer-implemented method also includes determining a retraining threshold for the individual-item model and calculating a new feature vector relating to new consumers. The new feature vector containing new attribute values of the new consumers and defined by the consumer feature vector. The computer-implemented method further includes determining a distance between the historical feature vector and the new feature vector and retraining the individual-item model upon determining that the distance between the historical feature vector and the new feature vector exceeds the retraining threshold.
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公开(公告)号：US11416770B2
公开(公告)日：2022-08-16
申请号：US16666728
申请日：2019-10-29
发明人： Michael Peran , Josh Price , Daniel Augenstern , Rahul Nahar , Pankaj Srivastava
摘要： Embodiments of the present disclosure include a computer-implemented method and system for determining when to retrain an individual-item model within a recommendation engine. The computer-implemented method includes defining a consumer feature vector having attributes of historical consumers that impact an individual-item model. The computer-implemented method further includes calculating a historical feature vector relating to the historical consumers. The computer-implemented method also includes determining a retraining threshold for the individual-item model and calculating a new feature vector relating to new consumers. The new feature vector containing new attribute values of the new consumers and defined by the consumer feature vector. The computer-implemented method further includes determining a distance between the historical feature vector and the new feature vector and retraining the individual-item model upon determining that the distance between the historical feature vector and the new feature vector exceeds the retraining threshold.
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