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US 11,386,442 B2Granted
Online behavioral predictor
AssigneeLiveperson, Inc.
InventorsShlomo Lahav, Ofer Ron, David Shimon
Life of this patent
Estimating the term…
Abstract
In some embodiments, a set of user groups can be defined, with each group relating to a different webpage experience, user action, etc. Requests are assigned to one of the groups based on actual webpage presentation features and/or user actions. A group-specific model is generated for each group and translates user information to a preliminary result (e.g., a purchasing probability). A model combination includes a weighted combination of a set of available group-specific models. User information is processed using the model combination to generate a model result. The model result is evaluated to determine whether a requested webpage is to be customized in a particular manner and/or an opportunity is to be offered.
Claims20
A computer-implemented method, comprising:
- defining a set of user groups, wherein each user group is defined according to historical data corresponding to a common experience during a website visit, and wherein each user group is associated with an action on a webpage;
- generating a set of group-specific models, each group-specific model corresponding to a particular user group of the set of user groups, wherein each group-specific model is generated by training that group-specific model using the historical data of the particular user group, and wherein each group-specific model is configured to generate a prediction of a likelihood that a user in the user group will complete an action associated with the user group;
- generating a model combination function configured to generate a model result from predictions generated by a plurality of group-specific models of the set of group-specific models, wherein the model combination function is generated by:
- assigning weights to the predictions generated by the plurality of group-specific models, wherein a weight assigned to each prediction is based on a data size of the historical data used to train the group-specific model that generated that prediction;
- and wherein predictions generated from a group-specific model with a larger data size of historical data are assigned a higher weight and predictions generated from a group-specific model with a smaller data size of historical data are assigned a lower weight;
- deriving a weighted sum from the weights and the corresponding predictions;
- receiving a new request for the webpage from a new user;
- identifying new user information associated with the new request;
- determining, from the new user information, user characteristics corresponding to the new user;
- generating a new user prediction in real-time by generating new predictions using the user characteristics as input to the plurality of group-specific models and applying the model combination function to the new predictions, wherein the new user prediction includes a probability that the new user will complete an action on the webpage, and wherein the action is associated with the user group;
- customizing the webpage for the new user to increase a likelihood that the new user will complete the action on the webpage according to an evaluation of the new user prediction;
- and
- transmitting the customized webpage in response to the new request.
10 more elements
A system, comprising:
- one or more data processors;
- and
- a non-transitory computer readable storage medium containing instructions which when executed on the one or more data processors, cause the one or more data processors to perform operations including:
- defining a set of user groups, wherein each user group is defined according to historical data corresponding to a common experience during a website visit, and wherein each user group is associated with an action on a webpage;
- generating a set of group-specific models, each group-specific model corresponding to a particular user group of the set of the set of user groups, wherein each group-specific model is generated by training that group-specific model using the historical data of the particular user group, and wherein each group-specific model is configured to generate a prediction of a likelihood that a user in the user group will complete an action associated with the user group;
- generating a model combination function configured to generate a model result from predictions generated by a plurality of group-specific models of the set of group-specific models, wherein the model combination function is generated by:
- assigning weights to the predictions generated by the plurality of group-specific models, wherein a weight assigned to each prediction is based on a data size of the historical data used to train the group-specific model that generated the prediction;
- and wherein predictions generated from a group-specific model with a larger data size of historical data are assigned a higher weight and predictions generated from a group-specific model with a smaller data size of historical data are assigned a lower weight;
- and
- deriving a weighted sum from the weights and the corresponding predictions;
- receiving a new request for the webpage from a new user;
- identifying new user information associated with the new request;
- determining, from the new user information, user characteristics corresponding to the new user;
- generating a new user prediction in real-time by generating new predictions using the user characteristics as input to the plurality of group-specific models and applying the model combination function to the new predictions, wherein the new user prediction includes a probability that the new user will complete an action on the webpage, and wherein the action is associated with the user group;
- customizing the webpage for the new user to increase a likelihood that the new user will complete the action on the webpage according to an evaluation of the new user prediction;
- and
- transmitting the customized webpage in response to the new request.
14 more elements
A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform operations including:
- defining a set of user groups, wherein each user group is defined according to historical data corresponding to a common experience during a website visit, and wherein each user group is associated with an action on a webpage;
- generating a set of group-specific models, each group-specific model corresponding to a particular user group of the set of user groups, wherein each group-specific model is generated by training that group-specific model using the historical data of the particular user group, and wherein each group-specific model is configured to generate a prediction of a likelihood that a user in the user group will complete an action associated with the user group;
- generating a model combination function configured to generate a model result from predictions generated by a plurality of group-specific models of the set of group-specific models, wherein the model combination function is generated by:
- assigning weights to the predictions generated by the plurality of group-specific models, wherein a weight assigned to each prediction is based on a data size of the historical data used to train the group-specific model that generated that prediction;
- and wherein predictions generated from a group-specific model with a larger data size of historical data are assigned a higher weight and predictions generated from a group-specific model with a smaller data size of historical data are assigned a lower weight
- deriving a weighted sum from the weights and the corresponding predictions;
- receiving a new request for the webpage from a new user;
- identifying new user information associated with the new request;
- determining, from the new user information, user characteristics corresponding to the new user;
- generating a new user prediction in real by generating new predictions using the user characteristics as input to the plurality of group-specific models and applying the model combination function to the new predictions, wherein the new user prediction includes a probability that the new user will complete an action on the webpage, and wherein the action is associated with the user group;
- customizing the webpage for the new user to increase a likelihood that the new user will complete the action on the webpage according to an evaluation of the new user prediction;
- and
- transmitting the customized webpage in response to the new request.
10 more elements
Dependencies are inferred from the claim text.