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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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2.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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3.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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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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