公开(公告)号：US20170330114A1

公开(公告)日：2017-11-16

申请号：US15156008

申请日：2016-05-16

Applicant: ADOBE SYSTEMS INCORPORATED

Inventor： Mohammad Ghavamzadeh ,  Alan John Malek ,  Yinlam Chow ,  Sumeet Katariya

IPC: G06Q10/06 ,  G06Q30/02

CPC classification number: G06Q10/067 ,  G06Q30/0201

Abstract: Embodiments of the present invention are directed at providing a sequential multiple hypothesis testing system. In one embodiment, feedback is collected for hypothesis tests of a multiple hypothesis tests. Based on the collected feedback, a sequential p-value is calculated for each of the hypothesis tests utilizing a sequential statistic procedure that is designed to compare an alternative case with a base case for a respective hypothesis test. A sequential rejection procedure can then be applied to determine whether any of the hypothesis tests have concluded based on the respective p-value. A result of the determination can then be output to apprise a user of a state of the multiple hypothesis test. This process can then be repeated until a maximum sample size is reached, termination criterion is met, or all tests are concluded. Other embodiments may be described and/or claimed.

公开(公告)号：US20170323329A1

公开(公告)日：2017-11-09

申请号：US15148920

申请日：2016-05-06

Applicant: Adobe Systems Incorporated

Inventor： Sumeet Katariya ,  Yinlam Chow ,  Mohammad Ghavamzadeh

IPC: G06Q30/02 ,  G06N7/00 ,  G06N5/02

CPC classification number: G06Q30/0244 ,  G06N5/003 ,  G06N7/005

Abstract: Sequential hypothesis testing techniques are described, which involve testing sequences of increasingly larger number of samples until a winner is determined. In particular, sequential hypothesis testing techniques is based on whether a result of a statistic has reached statistical significance that defines a confidence level in the accuracy of the results. Sequential hypothesis testing also permits the user to “peek” into the test through use of a user interface (e.g., dashboard) to monitor the test in real time as it is being run. Real time output of this information in a user interface as a part of sequential hypothesis testing may be leveraged in a variety of ways. In a first example, a user may make changes as the test is run. In another example, flexible execution is also made possible in that the test may continue to run even if initial accuracy guarantees have been met.

公开(公告)号：US20180349961A1

公开(公告)日：2018-12-06

申请号：US15611597

申请日：2017-06-01

Applicant: Adobe Systems Incorporated

Inventor： Sharan Vaswani ,  Zheng Wen ,  Branislav Kveton ,  Mohammad Ghavamzadeh

IPC: G06Q30/02 ,  G06Q50/00 ,  G06Q10/06

Abstract: Influence maximization determination within a social network system is described. In one example, a subset is selected from a plurality of user accounts of a social network system. Exposure of digital marketing content is then caused to the subset of user accounts. A determination is made as to a probability of each user account of the plurality of user accounts as being influenced by the exposure of the digital marketing content to the subset of user accounts. The determined probability is then output, such as to control output of digital marketing content.

公开(公告)号：US20180225589A1

公开(公告)日：2018-08-09

申请号：US15424695

申请日：2017-02-03

Applicant: Adobe Systems Incorporated

Inventor： Mohammad Ghavamzadeh ,  Abbas Kazerouni

IPC: G06N99/00

CPC classification number: G06N20/00 ,  G06F16/9535 ,  G06Q30/0241 ,  G06Q30/0271 ,  G06Q40/06

Abstract: A digital medium environment includes an action processing application that performs actions including personalized recommendation. A learning algorithm operates on a sample-by-sample basis (e.g., each instance a user visits a web page) and recommends an optimistic action, such as an action found by maximizing an expected reward, or a base action, such as an action from a baseline policy with known expected reward, subject to a safety constraint. The safety constraint requires that the expected performance of playing optimistic actions is at least as good as a predetermined percentage of the known performance of playing base actions. Thus, the learning algorithm is conservative during exploratory early stages of learning, and does not play unsafe actions. Furthermore, since the learning algorithm is online and can learn with each sample, it converges quickly and is able to track time varying parameters better than learning algorithms that learn on a block basis.

公开(公告)号：US20170323331A1

公开(公告)日：2017-11-09

申请号：US15148390

申请日：2016-05-06

Applicant: Adobe Systems Incorporated

Inventor： Alan John Malek ,  Yinlam Chow ,  Mohammad Ghavamzadeh

IPC: G06Q30/02

CPC classification number: G06Q30/0246

Abstract: Sample size determination techniques in sequential hypothesis testing in a digital medium environment are described. The sample size may be determined before a test to define a number of samples (e.g., user interactions with digital marketing content) that are likely to be tested as part of the sequential hypothesis testing in order to achieve a result. The sample size may also be determined in real time to define a number of samples that likely remain for testing in order to achieve a result. The sample size may be determined in a variety of ways, such as through simulation, based on a gap between conversion rates for different options being tested, and so on.

公开(公告)号：US20170206549A1

公开(公告)日：2017-07-20

申请号：US14997987

申请日：2016-01-18

Applicant: Adobe Systems Incorporated

Inventor： Sougata Chaudhuri ,  Georgios Theocharous ,  Mohammad Ghavamzadeh

IPC: G06Q30/02 ,  H04L29/08 ,  G06N99/00

CPC classification number: G06Q30/0246 ,  G06N20/00 ,  G06Q30/0255 ,  G06Q30/0277 ,  H04L67/22

Abstract: A digital medium environment is described to recommend advertisements using ranking functions. A ranking function is configured to compute a score by applying a user context vector associated with a user to individual ranking weight vectors associated with advertisements, and provide the advertisement with the highest score to the user. In order to learn the ranking weight vectors for the ranking function, training data is obtained that includes user interactions with advertisements during previous sessions as well as user context vectors. The ranking weight vectors for the ranking function associated with each advertisement can then be learned by controlling the score generated by the ranking function to be higher for positive interactions than the negative interactions. To do so, the ranking weight vectors may be learned by optimizing an area under the curve ranking loss (AUCL) for the ranking function.

公开(公告)号：US20180349466A1

公开(公告)日：2018-12-06

申请号：US15611563

申请日：2017-06-01

Applicant: Adobe Systems Incorporated

Inventor： Hamid Dadkhani ,  Mohammad Ghavamzadeh ,  Hung Bui ,  Branislav Kveton

IPC: G06F17/30 ,  G06F17/15 ,  G06N5/04 ,  G06F3/0484

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.

公开(公告)号：US20180276691A1

公开(公告)日：2018-09-27

申请号：US15465449

申请日：2017-03-21

Applicant: Adobe Systems Incorporated

Inventor： Chunyuan Li ,  Hung Hai Bui ,  Mohammad Ghavamzadeh ,  Georgios Theocharous

IPC: G06Q30/02 ,  G06N3/08

CPC classification number: G06Q30/0202 ,  G06N3/0445 ,  G06N3/08 ,  G06Q30/0205

Abstract: Metric forecasting techniques and systems in a digital medium environment are described that leverage similarity of elements, one to another, in order to generate a forecast value for a metric for a particular element. In one example, training data is received that describes a time series of values of the metric for a plurality of elements. The model is trained to generate the forecast value of the metric, the training using machine learning of a neural network based on the training data. The training includes generating dimensional-transformation data configured to transform the training data into a simplified representation to determine similarity of the plurality of elements, one to another, with respect to the metric over the time series. The training also includes generating model parameters of the neural network based on the simplified representation to generate the forecast value of the metric.

公开(公告)号：US20180082326A1

公开(公告)日：2018-03-22

申请号：US15269003

申请日：2016-09-19

Applicant: Adobe Systems Incorporated

Inventor： Nikolaos Vlassis ,  Mohammad Ghavamzadeh ,  Alan John Malek

IPC: G06Q30/02

CPC classification number: G06Q30/0242

Abstract: Paired testing techniques in a digital medium environment are described. A testing system receives data that describes user interactions, e.g., with digital content or other items. The data is organized by the testing system as pairs of user exposures to the different item. Filtering is then performed based on these pairs by the testing system to remove “tied” pairs. Tied pair are pairs of user interactions that result in the same output for binary data (e.g., converted or did not convert) or are within a defined threshold amount for continuous non-binary data. The filtered pair data is then tested, e.g., until criteria of a stopping rule are met as part of sequential hypothesis testing. The testing, for instance, may be used to evaluate which item of digital marketing content exhibits a greater effect, if any, on conversion and control subsequent deployment of this digital marketing content as a result.

公开(公告)号：US20160148246A1

公开(公告)日：2016-05-26

申请号：US14551898

申请日：2014-11-24

Applicant: Adobe Systems Incorporated

Inventor： Philip S. Thomas ,  Georgios Theocharous ,  Mohammad Ghavamzadeh

IPC: G06Q30/02

CPC classification number: G06Q30/0242 ,  G06N3/006 ,  G06N7/005 ,  G06N20/00 ,  G06Q30/0247

Abstract: Risk quantification, policy search, and automated safe policy deployment techniques are described. In one or more implementations, techniques are utilized to determine safety of a policy, such as to express a level of confidence that a new policy will exhibit an increased measure of performance (e.g., interactions or conversions) over a currently deployed policy. In order to make this determination, reinforcement learning and concentration inequalities are utilized, which generate and bound confidence values regarding the measurement of performance of the policy and thus provide a statistical guarantee of this performance. These techniques are usable to quantify risk in deployment of a policy, select a policy for deployment based on estimated performance and a confidence level in this estimate (e.g., which may include use of a policy space to reduce an amount of data processed), used to create a new policy through iteration in which parameters of a policy are iteratively adjusted and an effect of those adjustments are evaluated, and so forth.

Abstract translation: 描述风险量化，策略搜索和自动化安全策略部署技术。 在一个或多个实现中，利用技术来确定策略的安全性，例如表示新策略将相对于当前部署的策略表现出增加的性能测量（例如，交互或转换）的置信度。 为了做出这一决定，利用强化学习和集中不平等，产生和束缚关于策略绩效测量的置信度值，从而提供这种表现的统计保证。 这些技术可用于量化策略部署中的风险，根据估计的性能和置信水平选择部署策略（例如，可能包括使用策略空间来减少处理的数据量），使用 通过迭代创建一个新策略，在该策略中迭代地调整策略的参数，并评估这些调整的效果，等等。