US 9,317,812 B2Granted

Customized predictors for user actions in an online system

AssigneeFacebook, Inc.

InventorsIgor Kabiljo, Aleksandar Ilic, Ming Hua, Hong Yan

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Abstract

Online systems generate predictors for predicting actions of users of the online system. The online system receives requests to generate predictor models for predicting whether a user is likely to take an action of a particular action type. The request specifies the type of action and criteria for identifying a successful instance of the action type and a failure instance of the action type. The online system collects data including successful and failure instances of the action type. The online system generates one or more predictors of different types using the generated data. The online system evaluates and compares the performance of the different predictors generated and selects a predictor based on the performance. The online system returns a handle to access the generated predictor to the requester of the predictor.

Claims21

Dependent claims

A computer-implemented method comprising:

receiving, by an online system, a plurality of requests for generating predictors, each request specifying a type of action performed by users of the online system, a category of users, and criteria for identifying a successful instance of the type of action;
for each request associated with a type of action, generating, by the online system, a predictor for the type of action, the generating comprising:
generating a training data set from a plurality of successful instances and failure instances of the type of action performed by online system users selected in response to belonging to the specified category of users;
training one or more candidate predictors using the training data set, wherein each candidate predictor comprises a machine learned model;
evaluating performance of each candidate predictor by executing each predictor on a test data set generated from an additional plurality of successful instances and failure instances of the type of action performed by online system users selected in response to belonging to the specified category of users;
and
selecting a predictor for the type of action by comparing the performance of the one or more candidate predictors;
and
returning a handle corresponding to the generated predictor to a requester of the selected predictor;
periodically generating a new predictor by:
comparing performance of the new predictor against performance of a previously generated predictor;
and
responsive to the performance of the new predictor exceeding the performance of the previously generated predictor, replacing the previously generated predictor with the new predictor;
and
reducing a rate of periodically generating the new predictor for the action type responsive to an improvement in the performance of the new predictor compared to the performance of the previously generated predictor being below a threshold value.

12 more elements

A computer-implemented method comprising:

storing, by an online system, an existing predictor for predicting whether a user of the online system performs a type of action, the existing predictor associated with criteria for determining a successful instance of the type of action;
periodically regenerating the existing predictor by:
identifying a data set comprising successful instances and failure instances of the type of action performed by users of the online system;
training one or more candidate predictors using the identified data set, wherein each candidate predictor comprises a machine learned model;
evaluating performance of each candidate predictor by executing each predictor on a test data set comprising a plurality of successful instances and a plurality of failure instances of the type of action performed by users of the online system;
selecting a candidate predictor from the one or more candidate predictors by comparing the performance of the one or more candidate predictors;
comparing performance of the selected candidate predictor with performance of the existing predictor;
and
replacing the existing predictor by the selected candidate predictor responsive to performance of the selected candidate predictor exceeding the performance of the existing predictor;
and
reducing a rate of periodically regenerating the existing predictor for the action type responsive to an improvement in the performance of the selected predictor compared to the performance of the existing predictor being below a threshold value.

8 more elements

A computer program product having a non-transitory computer-readable storage medium storing computer-executable code, the code comprising:

a predictor factory module of an online system configured to:
store an existing predictor for predicting whether a user of the online system performs a type of action, the existing predictor associated with criteria for determining a successful instance of the type of action;
periodically regenerate the existing predictor by:
identifying a data set comprising successful instances and failure instances of the type of action performed by users of the online system;
training one or more candidate predictors using the identified data set, wherein each candidate predictor comprises a machine learned model;
evaluating performance of each candidate predictor by executing each predictor on a test data set comprising a plurality of successful instances and a plurality of failure instances of the type of action performed by users of the online system;
selecting a candidate predictor from the one or more candidate predictors by comparing the performance of the one or more candidate predictors;
comparing performance of the selected candidate predictor with performance of the existing predictor;
and
replacing the existing predictor by the selected candidate predictor responsive to performance of the selected candidate predictor exceeding the performance of the existing predictor;
and
reduce a rate of periodically regenerating the existing predictor for the action type responsive to an improvement in the performance of the selected predictor compared to the performance of the existing predictor being below a threshold value.

9 more elements

Dependencies are inferred from the claim text.