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公开(公告)号：US20200167690A1
公开(公告)日：2020-05-28
申请号：US16203263
申请日：2018-11-28
Applicant: Adobe Inc.
Inventor： Handong Zhao , Zheng Wen , Sungchul Kim , Sheng Li , Branislav Kveton
Abstract: Systems and techniques for multi-task equidistant embedding are described that process categorical feature data to explore feature interactions. A digital analytics system enforces an equidistant relationship among features within a category while extracting high-order feature interactions by punishing both positive correlations and negative correlations among low-dimensional representations of different features. By enforcing an equidistant embedding, information is retained and accuracy is increased while higher order feature interactions are determined. Further, the digital analytics system shares knowledge among different tasks by connecting a shared network representation common to multiple tasks with exclusive network representations specific to particular tasks.
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公开(公告)号：US20200074504A1
公开(公告)日：2020-03-05
申请号：US16121450
申请日：2018-09-04
Applicant: Adobe Inc.
Inventor： Yang Cao , Zheng Wen , Branislav Kveton
Abstract: Recommendation systems and techniques are described that employ change point detection to generate recommendations for digital content. In one example, a change point detection technique is employed by a recommendation system to identify when a change point has occurred at a respective time step of a series of time steps. Detection of this change point may then be used by the recommendation system to reset the statistical model to address this change as well as generate a subsequent recommendation configured for exploration of reward distributions of the items of digital marketing content.
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公开(公告)号：US12182713B2
公开(公告)日：2024-12-31
申请号：US16203263
申请日：2018-11-28
Applicant: Adobe Inc.
Inventor： Handong Zhao , Zheng Wen , Sungchul Kim , Sheng Li , Branislav Kveton
Abstract: Systems and techniques for multi-task equidistant embedding are described that process categorical feature data to explore feature interactions. A digital analytics system enforces an equidistant relationship among features within a category while extracting high-order feature interactions by punishing both positive correlations and negative correlations among low-dimensional representations of different features. By enforcing an equidistant embedding, information is retained and accuracy is increased while higher order feature interactions are determined. Further, the digital analytics system shares knowledge among different tasks by connecting a shared network representation common to multiple tasks with exclusive network representations specific to particular tasks.
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Patent Agency Ranking
公开(公告)号：US20230259829A1
公开(公告)日：2023-08-17
申请号：US18306449
申请日：2023-04-25
Applicant: Adobe Inc.
Inventor： Georgios Theocharous , Zheng Wen , Yasin Abbasi Yadkori , Qingyun Wu
CPC classification number: G06N20/00 , G06N5/04 , G06F18/2193
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing offline models to warm start online bandit learner models. For example, the disclosed system can determine relevant offline models for an environment based on reward estimate differences between the offline models and the online model. The disclosed system can then utilize the relevant offline models (if any) to select an arm for the environment. The disclosed system can update the online model based on observed rewards for the selected arm. Additionally, the disclosed system can also use entropy reduction of arms to determine the utility of the arms in differentiating relevant and irrelevant offline models. For example, the disclosed system can select an arm based on a combination of the entropy reduction of the arm and the reward estimate for the arm and use the observed reward to update an observation history.
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公开(公告)号：US11669768B2
公开(公告)日：2023-06-06
申请号：US16584082
申请日：2019-09-26
Applicant: Adobe Inc.
Inventor： Georgios Theocharous , Zheng Wen , Yasin Abbasi Yadkori , Qingyun Wu
CPC classification number: G06F18/2193 , G06N5/04 , G06N20/00
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing offline models to warm start online bandit learner models. For example, the disclosed system can determine relevant offline models for an environment based on reward estimate differences between the offline models and the online model. The disclosed system can then utilize the relevant offline models (if any) to select an arm for the environment. The disclosed system can update the online model based on observed rewards for the selected arm. Additionally, the disclosed system can also use entropy reduction of arms to determine the utility of the arms in differentiating relevant and irrelevant offline models. For example, the disclosed system can select an arm based on a combination of the entropy reduction of the arm and the reward estimate for the arm and use the observed reward to update an observation history.
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公开(公告)号：US10984058B2
公开(公告)日：2021-04-20
申请号：US15892085
申请日：2018-02-08
Applicant: Adobe Inc.
Inventor： Branislav Kveton , Zheng Wen , Prakhar Gupta , Iftikhar Ahamath Burhanuddin , Harvineet Singh , Gaurush Hiranandani
IPC: G06F16/00 , G06F16/9535 , G06N20/00 , G06F16/248 , G06F16/2457 , G06Q30/02
Abstract: A machine-learning framework uses partial-click feedback to generate an optimal diverse set of items. An example method includes estimating a preference vector for a user based on diverse cascade statistics for the user, the diverse cascade statistics including previously observed responses and previously observed topic gains. The method also includes generating an ordered set of items from the item repository, the items in the ordered set having highest topic gain weighted by similarity with the preference vector, providing the ordered set for presentation to the user, and receiving feedback from the user on the ordered set. The method also includes, responsive to the feedback indicating a selected item, updating the diverse cascade statistics for observed items, wherein the updating results in penalizing the topic gain for items of the observed items that are not the selected item and promoting the topic gain for the selected item.
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17.发明申请公开(公告)号：US20190311394A1
公开(公告)日：2019-10-10
申请号：US15944980
申请日：2018-04-04
Applicant: Adobe Inc.
Inventor： Branislav Kveton , Zheng Wen , Yasin Abbasi Yadkori , Mohammad Ghavamzadeh , Claire Vernade
IPC: G06Q30/02
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for determining parameters for digital campaign content in connection with executing digital campaigns using a rank-one assumption and a best-arm identification algorithm. For example, the disclosed system alternately explores response data in the first dimension and response data in the second dimension using the rank-one assumption and the best-arm identification algorithm to estimate highest sampling values from each dimension. In one or more embodiments, the disclosed system uses the estimated highest sampling values from the first and second dimension to determine a combination with a highest sampling value in a parameter matrix constructed based on the first dimension and the second dimension, and then executes the digital campaign using the determined combination.
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公开(公告)号：US20190243923A1
公开(公告)日：2019-08-08
申请号：US15892085
申请日：2018-02-08
Applicant: Adobe Inc.
Inventor： Branislav Kveton , Zheng Wen , Prakhar Gupta , Iftikhar Ahamath Burhanuddin , Harvineet Singh , Gaurush Hiranandani
CPC classification number: G06F16/9535 , G06F16/24578 , G06F16/248 , G06N20/00
Abstract: A machine-learning framework uses partial-click feedback to generate an optimal diverse set of items. An example method includes estimating a preference vector for a user based on diverse cascade statistics for the user, the diverse cascade statistics including previously observed responses and previously observed topic gains. The method also includes generating an ordered set of items from the item repository, the items in the ordered set having highest topic gain weighted by similarity with the preference vector, providing the ordered set for presentation to the user, and receiving feedback from the user on the ordered set. The method also includes, responsive to the feedback indicating a selected item, updating the diverse cascade statistics for observed items, wherein the updating results in penalizing the topic gain for items of the observed items that are not the selected item and promoting the topic gain for the selected item.
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