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公开(公告)号：US20190303995A1
公开(公告)日：2019-10-03
申请号：US15943807
申请日：2018-04-03
Applicant: Adobe Inc.
Inventor： Shuai Li , Zheng Wen , Yasin Abbasi Yadkori , Vishwa Vinay , Branislav Kveton
Abstract: The present disclosure is directed toward systems, methods, and computer readable media for training and utilizing an item-level importance sampling model to evaluate and execute digital content selection policies. For example, systems described herein include training and utilizing an item-level importance sampling model that accurately and efficiently predicts a performance value that indicates a probability that a target user will interact with ranked lists of digital content items provided in accordance with a target digital content selection policy. Specifically, systems described herein can perform an offline evaluation of a target policy in light of historical user interactions corresponding to a training digital content selection policy to determine item-level importance weights that account for differences in digital content item distributions between the training policy and the target policy. In addition, the systems described herein can apply the item-level importance weights to training data to train item-level importance sampling model.
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公开(公告)号：US11948095B2
公开(公告)日：2024-04-02
申请号：US16691158
申请日：2019-11-21
Applicant: ADOBE INC.
Inventor： Abhilasha Sancheti , Zheng Wen , Iftikhar Ahamath Burhanuddin
Abstract: A method for recommending digital content includes: determining user preferences and a time horizon of a given user; determining a group for the given user based on the determined user preferences; determining a number of users of the determined group and a similarity of the users; applying information including the number of users, the similarity, and the time horizon to a model selection classifier to select one of a personalized model of the user and a group model of the determined group; and running the selected model to determine digital content to recommend.
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公开(公告)号：US20210097350A1
公开(公告)日：2021-04-01
申请号：US16584082
申请日：2019-09-26
Applicant: Adobe Inc.
Inventor： Georgios Theocharous , Zheng Wen , Yasin Abbasi Yadkori , Qingyun Wu
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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4.发明申请公开(公告)号：US20190324606A1
公开(公告)日：2019-10-24
申请号：US15957706
申请日：2018-04-19
Applicant: Adobe Inc.
Inventor： Branislav Kveton , Zheng Wen , Hung Bui , Tong Yu
IPC: G06F3/0483 , G06N99/00 , H04L29/08 , G06F17/21
Abstract: Systems and methods for customizing an interactive experience based on topics determined from an online topic model. In an example, a segmentation application executing on a computing device accesses past user interaction vectors that represent interaction data from an electronic content delivery system. The segmentation application accesses a segmentation model having parameters. The segmentation application updates the parameters by performing tensor decomposition on a tensor built from the past user interaction vectors and calculating updating values of the parameters from the tensor decomposition. The segmentation application performs a segmentation of user devices by applying the segmentation model with the updated parameters to the present user interaction vector. The segmentation assigns the user device to the user segment. The segmentation application transmits data describing the segmentation to the electronic content delivery system.
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公开(公告)号：US11593860B2
公开(公告)日：2023-02-28
申请号：US16880168
申请日：2020-05-21
Applicant: ADOBE INC.
Inventor： Shuai Li , Zheng Wen , Yasin Abbasi Yadkori , Vishwa Vinay , Branislav Kveton
IPC: G06Q30/00 , G06Q30/0601 , G06Q30/0251 , G06N20/00
Abstract: The present disclosure is directed toward systems, methods, and computer readable media for training and utilizing an item-level importance sampling model to evaluate and execute digital content selection policies. For example, systems described herein include training and utilizing an item-level importance sampling model that accurately and efficiently predicts a performance value that indicates a probability that a target user will interact with ranked lists of digital content items provided in accordance with a target digital content selection policy. Specifically, systems described herein can perform an offline evaluation of a target policy in light of historical user interactions corresponding to a training digital content selection policy to determine item-level importance weights that account for differences in digital content item distributions between the training policy and the target policy. In addition, the systems described herein can apply the item-level importance weights to training data to train item-level importance sampling model.
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Patent Agency Ranking
6.发明授权公开(公告)号：US11551256B2
公开(公告)日：2023-01-10
申请号：US17334237
申请日：2021-05-28
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：US11062346B2
公开(公告)日：2021-07-13
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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8.发明申请公开(公告)号：US20210319470A1
公开(公告)日：2021-10-14
申请号：US17334237
申请日：2021-05-28
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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9.发明申请公开(公告)号：US20200286154A1
公开(公告)日：2020-09-10
申请号：US16880168
申请日：2020-05-21
Applicant: ADOBE INC.
Inventor： Shuai Li , Zheng Wen , Yasin Abbasi Yadkori , Vishwa Vinay , Branislav Kveton
Abstract: The present disclosure is directed toward systems, methods, and computer readable media for training and utilizing an item-level importance sampling model to evaluate and execute digital content selection policies. For example, systems described herein include training and utilizing an item-level importance sampling model that accurately and efficiently predicts a performance value that indicates a probability that a target user will interact with ranked lists of digital content items provided in accordance with a target digital content selection policy. Specifically, systems described herein can perform an offline evaluation of a target policy in light of historical user interactions corresponding to a training digital content selection policy to determine item-level importance weights that account for differences in digital content item distributions between the training policy and the target policy. In addition, the systems described herein can apply the item-level importance weights to training data to train item-level importance sampling model.
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10.发明授权公开(公告)号：US10706454B2
公开(公告)日：2020-07-07
申请号：US15943807
申请日：2018-04-03
Applicant: Adobe Inc.
Inventor： Shuai Li , Zheng Wen , Yasin Abbasi Yadkori , Vishwa Vinay , Branislav Kveton
Abstract: The present disclosure is directed toward systems, methods, and computer readable media for training and utilizing an item-level importance sampling model to evaluate and execute digital content selection policies. For example, systems described herein include training and utilizing an item-level importance sampling model that accurately and efficiently predicts a performance value that indicates a probability that a target user will interact with ranked lists of digital content items provided in accordance with a target digital content selection policy. Specifically, systems described herein can perform an offline evaluation of a target policy in light of historical user interactions corresponding to a training digital content selection policy to determine item-level importance weights that account for differences in digital content item distributions between the training policy and the target policy. In addition, the systems described herein can apply the item-level importance weights to training data to train item-level importance sampling model.
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