Abstract: Some embodiments described herein relate to a computer-implemented method that includes accessing behavioral data, such as web visitation data, of multiple users. A sparse behavioral vector can be defined for each user based on the behavioral data. Each element of each sparse behavioral vector can represent a different potential detectable behavior such that each sparse behavioral vector encodes the behavioral data for that user. Multiple supervised learning models to each sparse behavioral vector to densify the vectors, defining multiple dense behavioral vectors. An unsupervised machine learning technique can be applied to the dense behavioral vectors to cluster, or define subpopulations, based on similarities between the dense behavioral vectors. Delivery of targeted content to a user can be facilitated based on a dense behavioral vector associated with that user being associated with one or more of the clusters or subpopulations.

Abstract: Some embodiments described herein relate to a computer-implemented method that includes accessing behavioral data, such as web visitation data, of multiple users. A sparse behavioral vector can be defined for each user based on the behavioral data. Each element of each sparse behavioral vector can represent a different potential detectable behavior such that each sparse behavioral vector encodes the behavioral data for that user. Multiple supervised learning models to each sparse behavioral vector to densify the vectors, defining multiple dense behavioral vectors. An unsupervised machine learning technique can be applied to the dense behavioral vectors to cluster, or define subpopulations, based on similarities between the dense behavioral vectors. Delivery of targeted content to a user can be facilitated based on a dense behavioral vector associated with that user being associated with one or more of the clusters or subpopulations.

Abstract: A non-transitory processor-readable medium is provided that stores code representing instructions to be executed by a processor to receive data associated with access by a first plurality of entities to a first website location and to receive data associated with access by a second plurality of entities to a second website location. The processor is also caused to define a co-visitation factor for each of the first website location and the second website location based on the received data. The processor is also caused to, if the co-visitation factor of the first website location and/or the co-visitation factor of the second website location is over a predefined threshold, select the first website location and/or the second website location as target website locations. The processor is caused to send a signal to set a flag associated with each target website location indicating the target website location as a suspicious website location.

Abstract: A non-transitory processor-readable medium is provided that stores code representing instructions to be executed by a processor to receive data associated with access by a first plurality of entities to a first website location and to receive data associated with access by a second plurality of entities to a second website location. The processor is also caused to define a co-visitation factor for each of the first website location and the second website location based on the received data. The processor is also caused to, if the co-visitation factor of the first website location and/or the co-visitation factor of the second website location is over a predefined threshold, select the first website location and/or the second website location as target website locations. The processor is caused to send a signal to set a flag associated with each target website location indicating the target website location as a suspicious website location.

Abstract: A non-transitory processor-readable medium stores code representing instructions to be executed by a processor to receive data associated with access by a first plurality of entities to a first website location and to receive data associated with access by a second plurality of entities to a second website location. The processor is also caused to define a co-visitation factor for each of the first website location and the second website location based on the received data. The processor is also caused to, if the co-visitation factor of the first website location and/or the co-visitation factor of the second website location is over a predefined threshold, select the first website location and/or the second website location as target website locations. The processor is caused to send a signal to set a flag associated with each target website location indicating the target website location as a suspicious website location.

Abstract: Methods for bid optimization and inventory scoring are provided. The method comprises receiving a definition of a success event associated with a campaign that has a set of campaign parameters. The method also comprises calculating an impact estimate for a performance measure of the success event. The impact estimate is calculated based on a set of auction parameters and a set of historical data associated with a browser. The method further comprises defining a bidding function based on the impact estimate, a set of characteristics of the browser, and the set of campaign parameters. The method also comprises calculating, after calculating the impact estimate and defining the bidding function, a bid value using the bidding function. The method also comprises sending a signal having the bid value to a real-time bidding exchange.

Abstract: A method and system for customer-choice-based bundling of product options collects data from previous orders about customer component choices, computes a pairwise distance between any pair of components that capture how much the probability of a choice pair P(a.b) deviates from the expected probability under the null hypothesis of independence P(a)*P(b), and clusters the components. The methodology can be implemented as instructions implemented in a computer readable medium. In this way, the need for a method to permit bundles of product options to be configured through the use of business processes reflecting choices based on the preferences of customers rather than the preferences of product designers is fulfilled.

Abstract: A method and system perform ranking-based evaluations for regression models that are often appropriate for marketing tasks and are more robust to outliers than traditional residual-based performance measures. The output provided by the method and system provides visualization that can offer insights about local model performance and outliers. Several models can be compared to each other to identify the “best” model and, therefore, the “best” model data for the particular marketing task.

Abstract: A method and system for customer-choice-based bundling of product options collects data from previous orders about customer component choices, computes a pairwise distance between any pair of components that capture how much the probability of a choice pair P(a.b) deviates from the expected probability under the null hypothesis of independence P(a)*P(b), and clusters the components. The methodology can be implemented as instructions implemented in a computer readable medium. In this way, the need for a method to permit bundles of product options to be configured through the use of business processes reflecting choices based on the preferences of customers rather than the preferences of product designers is fulfilled.

Abstract: A method and system perform ranking-based evaluations for regression models that are often appropriate for marketing tasks and are more robust to outliers than traditional residual-based performance measures. The output provided by the method and system provides visualization that can offer insights about local model performance and outliers. Several models can be compared to each other to identify the “best” model and, therefore, the “best” model data for the particular marketing task.

Abstract: A method and system perform ranking-based evaluations for regression models that are often appropriate for marketing tasks and are more robust to outliers than traditional residual-based performance measures. The output provided by the method and system provides visualization that can offer insights about local model performance and outliers. Several models can be compared to each other to identify the “best” model and, therefore, the “best” model data for the particular marketing task.

Abstract: A method and system for customer-choice-based bundling of product options collects data from previous orders about customer component choices, computes a pairwise distance between any pair of components that capture how much the probability of a choice pair P(a.b) deviates from the expected probability under the null hypothesis of independence P(a)*P(b), and clusters the components. The methodology can be implemented as instructions implemented in a computer readable medium. In this way, the need for a method to permit bundles of product options to be configured through the use of business processes reflecting choices based on the preferences of customers rather than the preferences of product designers is fulfilled.