-
公开(公告)号：US20200242678A1
公开(公告)日：2020-07-30
申请号：US16847156
申请日：2020-04-13
Applicant: Adobe Inc.
Inventor： Hung Bui , Branislav Kveton , Suvash Sedhain , Nikolaos Vlassis , Jaya Kawale
Abstract: Techniques disclosed herein provide more efficient and more relevant item recommendations to users in large-scale environments in which only positive interest information is known. The techniques use a rank-constrained formulation that generalizes relationships based on known user interests in items and/or use a randomized singular value decomposition (SVD) approximation technique to solve the formulation to identify items of interest to users in an efficiently, scalable manner.
-
12.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.
-
13.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.
-
14.发明授权公开(公告)号：US11055317B2
公开(公告)日：2021-07-06
申请号：US15611563
申请日：2017-06-01
Applicant: Adobe Inc.
Inventor： Hamid Dadkhahi , Mohammad Ghavamzadeh , Hung Bui , Branislav Kveton
Abstract: Certain embodiments involve determining and outputting correlations between metrics in large-scale web analytics datasets. For example, a processor identifies pairs of data metrics in a web analytics data set and determines a Maximal Information Coefficient (MIC) score for each pair of data metrics that indicates a strength of a correlation between the pair of data metrics. The processor generates an interactive user interface that graphically displays each pair of correlated data metrics having an MIC score above a threshold and the interactive user interface indicates the strength of the correlation between each displayed pair of correlated data metrics. The processor receives user input indicating an adjustment to the threshold and modifies the interactive user interface in response to receiving the user input by adding pairs of correlated data metrics to, or removing pairs of correlated metrics from, the interactive user interface based on the adjustment to the threshold.
-
15.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.
-
Patent Agency Ranking
16.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.
-
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.
-
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.
-
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.
-
20.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.
-
-
-
-
-
-
-
-
-
Search Results
22
records
(took 0.032 seconds)
