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公开(公告)号：US20180018569A1
公开(公告)日：2018-01-18
申请号：US15207523
申请日：2016-07-12
发明人： Haggai Roitman , Oren Sar-Shalom
CPC分类号： G06N5/04 , G06F16/9535 , G06N20/00
摘要： Methods, computing systems and computer program products implement embodiments of the present invention that include identifying a set of the items in the transactions, and executing, using input including the transactions, an implicit feedback collaborative filtering model to compute, for each of the users, a predicted rating for each of the items. Using input including the transactions and the predicted ratings as labels, a sentiment analysis model is executed to compute, for each given user, an opinion for each of the given items in the transactions for the given user, and using input including the transactions and the opinions as factors for the predicted ratings, an explicit feedback collaborative analysis model is executed to update, for each of the users, the predicted ratings for each of the items.
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公开(公告)号：US11736428B2
公开(公告)日：2023-08-22
申请号：US16450567
申请日：2019-06-24
IPC分类号： H04L51/216 , G06N3/044 , G06N3/08
CPC分类号： H04L51/216 , G06N3/044 , G06N3/08
摘要： An approach is provided that receives a message and applies a deep analytic analysis to the message. The deep analytic analysis results in a set of enriched message embedding (EME) data that is passed to a trained neural network. Based on a set of scores received from the trained neural network, a conversation is identified from a number of available conversations to which the received message belongs. The received first message is then associated with the identified conversation.
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公开(公告)号：US20200344192A1
公开(公告)日：2020-10-29
申请号：US16391458
申请日：2019-04-23
摘要： An approach is provided that receives a message and applies a deep analytic analysis to the message. The deep analytic analysis results in a set of enriched message embedding (EME) data that is passed to a trained neural network. Based on a set of scores received from the trained neural network, a conversation is identified from a number of available conversations to which the received message belongs. The received first message is then associated with the identified conversation.
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公开(公告)号：US20190385060A1
公开(公告)日：2019-12-19
申请号：US16008058
申请日：2018-06-14
发明人： BOAZ CARMELI , Guy Hadash , Einat Kermany , Ofer Lavi , Guy Lev , Oren Sar-Shalom
摘要： During end-to-end training of a Deep Neural Network (DNN), a differentiable estimator subnetwork is operated to estimate a functionality of an external software application. Then, during inference by the trained DNN, the differentiable estimator subnetwork is replaced with the functionality of the external software application, by enabling API communication between the DNN and the external software application.
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公开(公告)号：US20190179914A1
公开(公告)日：2019-06-13
申请号：US15838397
申请日：2017-12-12
发明人： Shai Erera , Haggai Roitman , Oren Sar-Shalom , Bar Weiner
IPC分类号： G06F17/30
CPC分类号： G06F16/24578 , G06F16/3334 , G06F16/3346 , G06F16/93
摘要： A computer implemented method for estimating quality of document retrieval comprising: retrieving from a corpus of documents stored on at least one storage a plurality of digital documents which comply with a document retrieval query according to a retrieval model; computing a plurality of retrieval scores each calculated for one of the plurality of digital documents using a relevance function scoring a relevance of one of the retrieved plurality of digital documents to the query; computing a calibrated weighted product model (WPM) estimator by calculating a combination of the plurality of retrieval scores weighted according to a plurality of retrieval features of the corpus and/or the query and/or a document, wherein the plurality of retrieval features are weighted according to a relative importance; and using the calibrated WPM estimator to score the plurality of digital documents' relevance to the query.
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公开(公告)号：US20190146636A1
公开(公告)日：2019-05-16
申请号：US15811714
申请日：2017-11-14
IPC分类号： G06F3/0482 , G06N7/00 , G06F3/0484
摘要： The present invention provides a method, computer program product, and system of generating prioritized list. In an embodiment, the method, computer program product, and system include receiving, by a computer system, target user identification data identifying a target user, target action data, social network content for the one or more users, and social network activity data for the one or more users, analyzing, by a computer system, social network links between the source user and the target user and the social network activity data for the one or more users, determining, by a computer system, a prioritized list of probabilistic action paths that could result in the target user performing the target action on the content based on the analyzing, and outputting the prioritized list to the source user.
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公开(公告)号：US11677705B2
公开(公告)日：2023-06-13
申请号：US16391458
申请日：2019-04-23
IPC分类号： H04L51/216 , G06N3/08 , G06N3/044
CPC分类号： H04L51/216 , G06N3/044 , G06N3/08
摘要： An approach is provided that receives a message and applies a deep analytic analysis to the message. The deep analytic analysis results in a set of enriched message embedding (EME) data that is passed to a trained neural network. Based on a set of scores received from the trained neural network, a conversation is identified from a number of available conversations to which the received message belongs. The received first message is then associated with the identified conversation.
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公开(公告)号：US11188193B2
公开(公告)日：2021-11-30
申请号：US15811714
申请日：2017-11-14
IPC分类号： G06F3/0482 , G06N7/00 , G06F3/0484 , G06Q10/06 , G06Q50/00 , H04L29/08 , H04L12/26 , H04L12/24 , G06Q40/00
摘要： The present invention provides a method, computer program product, and system of generating prioritized list. In an embodiment, the method, computer program product, and system include receiving, by a computer system, target user identification data identifying a target user, target action data, social network content for the one or more users, and social network activity data for the one or more users, analyzing, by a computer system, social network links between the source user and the target user and the social network activity data for the one or more users, determining, by a computer system, a prioritized list of probabilistic action paths that could result in the target user performing the target action on the content based on the analyzing, and outputting the prioritized list to the source user.
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公开(公告)号：US10956816B2
公开(公告)日：2021-03-23
申请号：US15635272
申请日：2017-06-28
发明人： Amir Kantor , Oren Sar-Shalom , Guy Uziel
摘要： A method, computer system, and a computer program product for enhanced rating predictions is provided. The present invention may include receiving a user input. The present invention may then include translating the received user input into an embedding matrix and inputting the embedding matrix into a deep neural network. The present invention may further include generating, by the deep neural network, an output vector, a user bias term and an item bias term based on the embedding matrix. The present invention may then include calculating a predicted rating based on the generated output vector, the generated user bias term and the generated item bias term. The present invention may then include determining an accuracy of the predicted rating.
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公开(公告)号：US10831770B2
公开(公告)日：2020-11-10
申请号：US15838397
申请日：2017-12-12
发明人： Shai Erera , Haggai Roitman , Oren Sar-Shalom , Bar Weiner
IPC分类号： G06F16/2457 , G06F16/93 , G06F16/33 , G06F16/951 , G06F16/332 , G06F16/248 , G06F16/9535
摘要： A computer implemented method for estimating quality of document retrieval comprising: retrieving from a corpus of documents stored on at least one storage a plurality of digital documents which comply with a document retrieval query according to a retrieval model; computing a plurality of retrieval scores each calculated for one of the plurality of digital documents using a relevance function scoring a relevance of one of the retrieved plurality of digital documents to the query; computing a calibrated weighted product model (WPM) estimator by calculating a combination of the plurality of retrieval scores weighted according to a plurality of retrieval features of the corpus and/or the query and/or a document, wherein the plurality of retrieval features are weighted according to a relative importance; and using the calibrated WPM estimator to score the plurality of digital documents' relevance to the query.
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