The disclosed embodiments provide a system for evaluating a performance of a mobile application. During operation, the system obtains, for a statistical model used in a quasi-experimental design, a first predicted outcome produced from a first set of data that is collected from two substantially identical versions of a mobile application. Next, the system uses the first predicted outcome to assess a bias of the statistical model. The system then improves an accuracy of the statistical model by using the assessed bias to normalize a second predicted outcome of the statistical model.

Model validation and bias removal in quasi-experimental testing of mobile applications

The disclosed embodiments provide a system for evaluating a performance of a mobile application During operation, the system obtains a first set of data associated with adopters of a new version of a mobile application in a partial rollout of the new version and a second set of data associated with non-adopters of the new version in the partial rollout Next, the system applies a statistical model to the first and second sets of data to select a subset of the non-adopters as potential adopters of the new version The system then reduces a bias in a quasi-experimental design associated with the mobile application by using the first set of data and a third set of data associated with the potential adopters to estimate an average treatment effect (ATE) between the new version and an older version of the mobile application

Model-based matching for removing selection bias in quasi-experimental testing of mobile applications

The invention discloses a customer segmentation method and device based on cluster analysis. The method comprises the following steps: acquiring a customer information original data set to perform numerical preprocessing so as to obtain a data sample; performing dimension-reduction and feature extraction on the data sample through an automatic encoder, performing the variation coefficient method on the data sample processed by the automatic encoder so as to compute the weight of the attribute feature, and computing the distance between the sample points by adopting the weighted Euclidean distance formula; computing the average distance among all data samples, traversing an adjacent point, wherein the distance between each sample point and the adjacent point is less than the average distance, and counting all sample adjacent point amount and sorting according to descending order to determine k initial cluster center points; and clustering the remaining data according to the weighted Euclidean distance formula to accomplish the customer segmentation method.

Customer segmentation method and device based on cluster analysis

Methods and systems for providing digital assistance. One system includes at least one electronic processor configured to access data representing historical tasks performed by a user through at least one user device, determine, based on the data, a first plurality of tasks associated with a life event of the user, and store an association between the first plurality of tasks and the life event. The electronic processor is also configured to, in response to a current occurrence of the life event experienced by the user, retrieve the association and generate a user interface for display to the user, the user interface including a second plurality of tasks for addressing the current occurrence of the life event based on the first plurality of tasks.

Personalized adaptive task framework for user life events

A method for spatially authoring data in a data processing system, may include constructing one or more input spatial geometry regions and iterating through each input spatial geometry region to create current cumulative result data and rejecting geometry groups from the current cumulative result data. The method may also include for each particular input spatial geometry region of the one or more input spatial geometry regions, constructing minimal-split BSP trees from the particular input spatial geometry region and current cumulative result data, performing geometry processing by applying an additive modeling comparison rule to keep geometry outside of the particular input spatial geometry region with the current cumulative result data, and keep geometry outside of the current cumulative result data with the particular input spatial geometry region, and performing geometry processing by applying a subtractive modeling comparison rule to keep geometry outside of the input spatial geometry region with the current cumulative result data, and keep geometry inside of the current cumulative result data with the particular input spatial geometry region. The method may further include generating final result geometry after iterating over all of the one or more input spatial geometry regions.

Real-time spatial authoring in augmented reality using additive and subtractive modeling

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.

Learning user preferences using sequential user behavior data to predict user behavior and provide recommendations

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.

Campaign Effectiveness Determination using Dimension Reduction

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.

Customer segmentation via consensus clustering

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.

Nikolaos Vlassis

Papers

Learning user preferences using sequential user behavior data to predict user behavior and provide recommendations

Campaign Effectiveness Determination using Dimension Reduction

Customer segmentation via consensus clustering

Testing an Effect of User Interaction with Digital Content in a Digital Medium Environment