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公开(公告)号：US20180121942A1
公开(公告)日：2018-05-03
申请号：US15342983
申请日：2016-11-03
发明人： Nikolaos Vlassis , Hongfu Liu
IPC分类号： G06Q30/02
CPC分类号： G06Q30/0204
摘要： The customer segmentation system includes a basic partition constructor to generate basic partitions of customers in an original feature space. Further, a partition space transformer in the customer segmentation system transforms the original feature space to an augmented partition space based on membership information of the customers into the basic partitions. Subsequently, a consensus clustering builder in the customer segmentation system determines consensus-based partitions of the customers in the augmented partition space. As such, robust and high-quality partitions for customer segmentation are achieved in the customer segmentation system.
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2.发明申请公开(公告)号：US20180144518A1
公开(公告)日：2018-05-24
申请号：US15356169
申请日：2016-11-18
发明人： Nikolaos Vlassis , Ehsan Amid
CPC分类号： G06T11/206 , G06F17/18
摘要： The present disclosure relates to a triplet embedding system that improves dimensionality reduction through exponential triplet embedding. In particular, the triplet embedding system employs heavy-tailed properties of t-exponential distributions and robust non-convex loss functions to improve visualizations in the presence of noisy data. In addition, the triplet embedding system uses triplet similarity weighting and improved sampling to improve and accelerate triplet embedding in large datasets. Overall, the triplet embedding system produces improved dimensionality reduction visualizations, which accurately reveal the underlying structure of the real-world high-dimensional datasets in lower-dimensional space.
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3.发明申请公开(公告)号：US20180082326A1
公开(公告)日：2018-03-22
申请号：US15269003
申请日：2016-09-19
IPC分类号： G06Q30/02
CPC分类号： G06Q30/0242
摘要： 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.
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公开(公告)号：US10127694B2
公开(公告)日：2018-11-13
申请号：US15356169
申请日：2016-11-18
发明人： Nikolaos Vlassis , Ehsan Amid
摘要： The present disclosure relates to a triplet embedding system that improves dimensionality reduction through exponential triplet embedding. In particular, the triplet embedding system employs heavy-tailed properties of t-exponential distributions and robust non-convex loss functions to improve visualizations in the presence of noisy data. In addition, the triplet embedding system uses triplet similarity weighting and improved sampling to improve and accelerate triplet embedding in large datasets. Overall, the triplet embedding system produces improved dimensionality reduction visualizations, which accurately reveal the underlying structure of the real-world high-dimensional datasets in lower-dimensional space.
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5.发明申请公开(公告)号：US20180129971A1
公开(公告)日：2018-05-10
申请号：US15348747
申请日：2016-11-10
IPC分类号： G06N99/00
CPC分类号： G06N20/00 , G06N7/005 , G06Q30/0255
摘要： Certain embodiments involve learning user preferences and predicting user behavior based on sequential user behavior data. For example, a system obtains data about a sequence of prior actions taken by multiple users. The system determines a similarity between a prior action taken by the various users and groups the various users into groups or clusters based at least in part on the similarity. The system trains a machine-learning algorithm such that the machine-learning algorithm can be used to predict a subsequent action of a user among the various users based on the various clusters. The system further obtains data about a current action of a new user and determines which of the clusters to associate with the new user based on the new user's current action. The system determines an action to be recommended to the new user based on the cluster associated with the new user. The action can include a series or sequence of actions to be taken by the new user. The system further provides the series or sequence of actions or an action of the series or sequence to the new user.
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公开(公告)号：US20170140417A1
公开(公告)日：2017-05-18
申请号：US14945265
申请日：2015-11-18
发明人： Sheng Li , Nikolaos Vlassis , Jaya B. Kawale
IPC分类号： G06Q30/02
CPC分类号： G06Q30/0243
摘要： Campaign effectiveness determination techniques and systems are described that are usable to determine campaign effectiveness with improved accuracy and computing performance by reduction of confounding bias through dimension reduction. In one example, campaign data that pertains to first and second campaign groups is characterized using a plurality of features that describe subjects included in the first and second campaign groups. The characterized campaign data is projected, automatically and without user intervention, for the first and second campaign groups into a reduced dimension space, e.g., using linear or non-linear techniques. Subjects in the first and second campaign groups are associated, one to another using the projected campaign data, such that a number of subjects in the first campaign group is matched against a number of subjects in the second campaign group. Generation of a campaign effectiveness result is then controlled using the associated subjects in the first and second campaign groups.
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公开(公告)号：US20180165590A1
公开(公告)日：2018-06-14
申请号：US15373849
申请日：2016-12-09
CPC分类号： G06N7/005 , G06F16/9535 , G06N5/045 , G06N20/00
摘要： Certain embodiments involve generating personalized recommendations for users by inferring a propensity of each individual user to accept a recommendation. For example, a system generates a personalized user model based on a historical transition matrix that provides state transition probabilities from a general population of users. The probabilities are adjusted based on the propensity for a user to accept a recommendation. The system determines a recommended action for the user to transition between predefined states based on the user model. Once the user has performed an activity that transitions from a current state, the system adjusts a probability distribution for an estimate of the propensity based on whether the activity is the recommended action.
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公开(公告)号：US20170357988A1
公开(公告)日：2017-12-14
申请号：US15180582
申请日：2016-06-13
发明人： Trevor Paulsen , Craig Mathis , Nikolaos Vlassis , Branislav Kveton , Kristopher Paries , Ivan Andrus , Hung Bui , Michael Rimer
IPC分类号： G06Q30/02 , G06F3/0482
CPC分类号： G06Q30/0204 , G06F3/0484
摘要： Systems and methods are disclosed herein for providing a user interface representing differences between segments of end users. The systems and methods receive user input on a user interface identifying a first segment, the first segment being a subset of the end users having a particular characteristic, determine differences between the first segment and a second segment, and represent, on the user interface, the differences between the first segment and the second segment based on relative significances of the differences. The marketer using the user interface is able to quickly and easily identify the metrics, dimensions, and/or relationships to other segments that most distinguish the compared segments from one another.
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