Predicting behavior using features derived from statistical information

A computer readable storage medium having

• embodied thereon computer-usable instructions which, when executed by one or more processing devices, perform a method for utilizing a reduced-dimensionality feature space prediction model to present user-selectable content in an online environment based on a prediction of a future user behavior within the online environment, the method comprising:
• receiving a master dataset that provides plural training examples;
• producing plural bins based on a partitioning strategy;
• identifying plural subsets of data within the master dataset that pertain to the respective plural bins for a single aspect variable, wherein each subset of data corresponds to a respective bin, and wherein each subset of data comprises a respective set of aspect values for the aspect variable;
• for each bin, generating a respective instance of statistical information based on the subset of data in the bin, wherein the respective instance of statistical information corresponds to the bin, wherein each instance of statistical information is utilized as a feature to train a prediction model, wherein each feature corresponds to a plurality of aspect values for the single aspect variable, thereby providing a reduced dimensionality of a feature vector that is utilized to train a prediction model;
• training the prediction model, which predicts a future user behavior, based on the feature information and a set of training examples;
• receiving input information that describes a new event, the new event comprising an online environment that displays user-selectable items to a user,
• wherein the new event is associated with aspect variables that describe characteristics of the new event, and wherein each aspect variable is associated with a set of one or more possible aspect values;
• identifying aspect values associated with the new event;
• determining which of the instances of statistical information are associated with the identified aspect values, wherein the determined instances of statistical information constitute features that correspond to the new event;
• utilizing the prediction model to predict that the user will select a particular user-selectable item, based on the features that correspond to the new event;
• and
• based on the prediction that the user will select the particular user-selectable item, causing the particular user-selectable item to be presented to the user.

A system for utilizing a reduced-dimensionality feature space prediction model to present user-selectable content in an online environment based on a prediction of a future user behavior within the online environment, comprising:

• a data store that provides at least one lookup data structure, each lookup data structure being associated with a particular aspect associated with events,
• wherein an event comprises a plurality of characteristics and a user's decision within a circumstance to click on an object or not click on the object,
• wherein the event is associated with aspect variables that describe the characteristics of the event,
• wherein the event is associated with a label that identifies whether the user clicked on the object or declined to click on the object,
• wherein each aspect variable is associated with instances of statistical information that represent respective partitions of aspect values associated with the aspect variable;
• and each lookup data structure identifies:
• plural partitions associated with the particular aspect;
• and
• plural instances of statistical information associated with the plural partitions, each instance of statistical information corresponding to a subset of aspect values for the particular aspect;
• a feature lookup module comprising one or more processors configured to:
• receive input information that describes a new event, wherein the new event comprises an opportunity to predict whether a current user will click on a particular object in an online environment;
• based on the input information that describes the new event, identify one or more aspect values associated with the new event;
• and
• identify, using said at least one lookup data structure, statistical information that is associated with the new event, based on a correspondence between said one or more aspect values and the statistical information, to produce identified statistical information, wherein instances of the identified statistical information correspond respectively to features that describe the new event, wherein each feature corresponds to a plurality of aspect values, thereby providing a reduced dimensionality of a feature space that is utilized to train the prediction model;
• a prediction generation module comprising one or more processors configured to generate a prediction, for the new event, of whether the user will click on the particular object in the online environment, based on an input feature vector comprising the features that describe the new event;
• and
• an action taking module comprising one or more processors configured to cause content to be presented to the online environment, wherein the content is determined and presented based on the prediction of whether the user will click on the particular object in the online environment.