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公开(公告)号:US09767409B1
公开(公告)日:2017-09-19
申请号:US14673407
申请日:2015-03-30
Applicant: Amazon Technologies, Inc.
Inventor: Roshan Harish Makhijani , Benjamin Thomas Cohen , Grant Michael Emery , Madhu Madhava Kurup , Vijai Mohan
CPC classification number: G06F17/3082 , G06N3/0445
Abstract: Features are disclosed for identifying and routing items for tagging using a latent feature model, such as a recurrent neural network language model (RNNLM). The model may be trained to identify latent features for catalog items such as movies, books, food items, beverages, and the like. Based on similarities in latent features, tags previous assigned to items may be applied to untagged items. Application may be manual or automatic. In either case, resources need to be balances to ensure efficient tagging of items. The included features help to identify and direct these limited tagging resources.
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公开(公告)号:US09615136B1
公开(公告)日:2017-04-04
申请号:US13887107
申请日:2013-05-03
Applicant: Amazon Technologies, Inc.
Inventor: Grant Michael Emery , Rahul Hemant Bhagat , Brian Cameros , Benjamin Thomas Cohen , Logan Luyet Dillard , Yongwen Liang , Scott Allen Mongrain , Michael David Quinn , Eli Glen Rosofsky , Adam Callahan Sanders
IPC: H04N7/173 , H04N21/472 , H04N21/258 , H04N21/442 , H04N5/445
CPC classification number: H04N21/47202 , H04N21/25891 , H04N21/44222 , H04N21/4756 , H04N21/4828
Abstract: The present technology may identify item category affinities by identifying a plurality of classifications of an item. An accuracy of the plurality of classifications relative to one another for the item may be identified. A category affinity of the item may be determined based on the accuracy of the plurality of classifications relative to one another.
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公开(公告)号:US09864951B1
公开(公告)日:2018-01-09
申请号:US14673384
申请日:2015-03-30
Applicant: Amazon Technologies, Inc.
Inventor: Roshan Harish Makhijani , Benjamin Thomas Cohen , Grant Michael Emery , Vijai Mohan
CPC classification number: G06N3/0445 , G06F17/276 , G06Q30/00
Abstract: Features are disclosed for identifying randomized latent feature language modeling, such as a recurrent neural network language modeling (RNNLM). Sequences of item identifiers may be provided as the language for training the language model where the item identifiers are the words of the language. To avoid localization bias, the sequences may be randomized prior to or during the training process to provide more accurate prediction models.
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