Systems and methods for generating and communicating application recommendations at uninstall time

• US 10,262,265 B2
• Filed: 05/24/2017
• Issued: 04/16/2019
• Est. Priority Date: 05/24/2017
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google LLC (Alphabet Inc.)
• Inventors: Lu, Hao
• Primary Examiner(s): Zhen, Wei Y
• Assistant Examiner(s): Ung, Lanny N
• Application Number:
  US15/604,647
• Publication Number:
  US 20180341865A1
• Time in Patent Office: 692 Days
• Field of Search: 717174
• CPC Class Codes: G06F 8/61 Installation
• CPC Class Codes: G06F 8/62 Uninstallation
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06Q 30/0631 Item recommendations

Abstract

A method for generating an application recommendation includes receiving a signal from an electronic device indicating that uninstallation of a first application has been initiated by a user on the electronic device, obtaining contextual information of the application, the contextual information including data indicating when the application was installed and frequency of use of the application, obtaining utilization data, the utilization data including data indicating applications that have been installed on the electronic device, determining a plurality of applications that are similar to the first application; generating a first predictive model using the contextual information and the utilization data, the first predictive model being configured to predict a likelihood of uninstallation of an application; and automatically recommending, at the time of uninstallation of the application, one or more of the plurality of applications, the recommending being based at least in part on the first predictive model.

Claims

1. A computer-implemented method for generating and communicating an application recommendation, comprising:

receiving a signal from an electronic device indicating that an uninstallation of an application has been initiated by a user of the electronic device;

obtaining contextual attribute information of the application, the contextual attribute information including data that indicates a period of time during which the application was installed and performance data of the application, the performance data including one or more of battery energy consumption, network data utilization, memory utilization, crash frequency, display metrics, or application load time metrics;

obtaining utilization data, the utilization data including data that indicates other applications that have been installed on the electronic device and historical usage of the other applications by the user;

determining, based at least in part of functionality provided by the application and respective functionality provided by a plurality of other applications, a portion of the plurality of applications as a plurality of similar application;

generating a predictive model using at least the contextual attribute information and the utilization data;

determining a respective predicted satisfaction score for each of the plurality of similar applications by at least applying the predictive model to the plurality of similar applications, wherein the predicted score represents a normalized value that a corresponding application will remain installed should the application be installed at the electronic device;

identifying one or more of the plurality of similar applications as having the respective predicted satisfaction score greater than a threshold satisfaction score as one or more recommend applications;

after receiving the signal indicating that the uninstallation of the application has been initiated, sending, to the electronic device, information about the one or more recommended applications as the application recommendation;

receiving, from the electronic device, a selection of one of the one or more recommended applications as an application to install; and

initiating installation of the application to install at the electronic device.

2. The method of claim 1, wherein the utilization data further includes data that indicates, respectively, for each of the other applications respectively, one or more of:

an installation time or an uninstallation time.

3. The method of claim 1, wherein the plurality of applications is stored in a distribution service platform and is determined based on metadata associated with the first application and metadata associated with the plurality of applications.

4. The method of claim 1, wherein sending the information about the one or more recommended applications further comprises:

causing a notification to be output on a display of the electronic device, the notification including one or more respective icons corresponding to the one or more recommended applications, wherein the electronic device is configured to initiate a purchase or download of the corresponding application when the respective icon is selected.

5. The method of claim 1, wherein generating the predictive model comprises:

determining a dataset comprising a matrix of one or more applications from the plurality of other applications, respective contextual attribute information for the one or more applications from the plurality of other applications, and respective utilization data information for the one or more applications from the plurality of other applications;

generating, based on the respective utilization data for each of the one or more applications from the plurality of other applications, respective satisfaction scores for each of the one or more application from the plurality of other applications, the respective satisfaction scores indicating a level of the user'"'"'s satisfaction with each of the one or more applications from the plurality of other applications;

dividing the dataset into at least one training dataset and at least one validation dataset; and

evaluating two or more machine learning algorithms on the training dataset and the validation dataset to determine an algorithm that produces the predictive model that predicts a target application'"'"'s score based on the target application'"'"'s corresponding contextual data.

6. The method of claim 5, wherein the two or more machine learning algorithms are selected from the group consisting of:

k-nearest neighbors, logistic regression, classification and regression trees, linear discriminant analysis, Gaussian naive Bayes and support vector machines.

7. The method of claim 5, wherein evaluating the two or more machine learning algorithms comprises:

applying the two or more algorithms to a first subset of the training dataset;

applying the two or more algorithms to a second subset of training dataset different from the first subset; and

selecting the algorithm and subset that produces the most accurate results against the validation dataset.

8. A system comprising:

a processor; and

a computer readable medium that stores computer readable instructions that, when executed by the processor, are effective to cause the system to;

receive a signal from an electronic device, the signal indicating that an uninstallation of an application has been initiated by a user of the electronic device;

obtain contextual attribute information of the application, the contextual attribute information including data that indicates a period of time during which the application was installed and performance data of the application, the performance data including one or more of battery energy consumption, network data utilization, memory utilization, crash frequency, display metrics, or application load time metrics;

obtain utilization data, the utilization data including data that indicates other applications that have been installed on the electronic device and historical usage of the other applications by the user;

determine, based at least in part of functionality provided by the application and respective functionality provided by a plurality of other applications, a portion of the plurality of applications as a plurality of similar application;

generate a predictive model using at least the contextual attribute information and the utilization data;

determine a respective predicted satisfaction score for each of the plurality of similar applications by at least applying the predictive model to the plurality of similar applications, wherein the predicted score represents a normalized value that a corresponding application will remain installed should the application be installed at the electronic device;

identify one or more of the plurality of similar applications as having the respective predicted satisfaction score greater than a threshold satisfaction score as one or more recommend applications;

after receiving the signal indicating that the uninstallation of the application has been initiated, send, to the electronic device, information about the one or more recommended applications as the application recommendation;

receive, from the electronic device, a selection of one of the one or more recommended applications as an application to install; and

initiate installation of the application to install at the electronic device.

9. The system of claim 8, wherein the utilization data further includes data that indicates, for each of the other applications respectively, one or more of:

an installation time or an uninstallation time.

10. The system of claim 8, wherein the plurality of applications is stored in a distribution service platform and is determined based on metadata associated with the first application and metadata associated with the plurality of applications.

11. The system of claim 8, wherein the computer readable instructions that cause the system to send the information about the one or more recommended applications further causes the electronic device to display a notification on a display of the electronic device, the notification including one or more respective icons corresponding to the one or more recommended applications.

12. The system of claim 8, wherein the computer readable instructions that cause the system to generate the predictive model causes the system to:

determine a dataset comprising a matrix of one or more applications from the plurality of other applications, respective contextual attribute information for the one or more applications from the plurality of other applications, and respective utilization data information for the one or more applications from the plurality of other applications;

generate, based on the respective utilization data for each of the one or more applications from the plurality of other applications, respective satisfaction scores for each of the one or more application from the plurality of other applications, the respective satisfaction scores indicating a level of the user'"'"'s satisfaction with each of the one or more applications from the plurality of other applications;

divide the dataset into at least one training dataset and at least one validation dataset; and

evaluate two or more machine learning algorithms on the training dataset and the validation dataset to determine an algorithm that produces the predictive model that predicts a target application'"'"'s score based on the target application'"'"'s corresponding contextual data.

13. The system of claim 12, wherein the two or more machine learning algorithms are selected from the group consisting of:

k-nearest neighbors, logistic regression, classification and regression trees, linear discriminant analysis, Gaussian naive Bayes and support vector machines.

14. The system of claim 12, wherein the computer readable instructions that cause the system to evaluate the two or more machine learning algorithms cause the system to:

apply the two or more algorithms to a first subset of the training dataset;

apply the two or more algorithms to a second subset of training dataset different from the first subset; and

select the algorithm and subset that produces the most accurate results against the validation dataset.

15. A non-transitory computer-readable storage medium encoded with instructions that, when executed, cause one or more processors to:

receive a signal from an electronic device, the signal indicating that an uninstallation of an application has been initiated by a user of the electronic device;

obtain contextual attribute information of the application, the contextual attribute information including data that indicates a period of time during which the application was installed and performance data of the application, the performance data including one or more of battery energy consumption, network data utilization, memory utilization, crash frequency, display metrics, or application load time metrics;

obtain utilization data, the utilization data including data that indicates other applications that have been installed on the electronic device and historical usage of the other applications by the user;

determine, based at least in part of functionality provided by the application and respective functionality provided by a plurality of other applications, a portion of the plurality of applications as a plurality of similar application;

generate a predictive model using at least the contextual attribute information and the utilization data;

determine a respective predicted satisfaction score for each of the plurality of similar applications by at least applying the predictive model to the plurality of similar applications, wherein the predicted score represents a normalized value that a corresponding application will remain installed should the application be installed at the electronic device;

identify one or more of the plurality of similar applications as having the respective predicted satisfaction score greater than a threshold satisfaction score as one or more recommend applications;

after receiving the signal indicating that the uninstallation of the application has been initiated, send, to the electronic device, information about the one or more recommended applications as the application recommendation;

receive, from the electronic device, a selection of one of the one or more recommended applications as an application to install; and

initiate installation of the application to install at the electronic device.

16. The non-transitory computer-readable storage medium of claim 15, wherein the utilization data further includes data that indicates, for the other applications respectively, one or more of:

an installation time, or an uninstallation time.

17. The non-transitory computer-readable storage medium of claim 15, wherein the plurality of applications is stored in a distribution service platform and are determined based on metadata associated with the first application and metadata associated with the plurality of applications.

18. The non-transitory computer-readable storage medium of claim 15, wherein the computer readable instructions that cause the one or more processors to send the information about the one or more recommended applications further causes the electronic device to display a notification on a display of the electronic device, the notification including one or more respective icons corresponding to the one or more recommended applications.

19. The non-transitory computer-readable storage medium of claim 15, wherein the computer readable instructions that cause the one or more processors to generate the predictive model causes the one or more processors to:

determine a dataset comprising a matrix of one or more applications from the plurality of other applications, respective contextual attribute information for the one or more applications from the plurality of other applications, and respective utilization data information for the one or more applications from the plurality of other applications;

generate, based on the respective utilization data for each of the one or more applications from the plurality of other applications, respective satisfaction scores for each of the one or more application from the plurality of other applications, the respective satisfaction scores indicating a level of the user'"'"'s satisfaction with each of the one or more applications from the plurality of other applications;

divide the dataset into at least one training dataset and at least one validation dataset; and

evaluate two or more machine learning algorithms on the training dataset and the validation dataset to determine an algorithm that produces the predictive model that predicts a target application'"'"'s score based on the target application'"'"'s corresponding contextual data.

20. The non-transitory computer-readable storage medium of claim 19, wherein the two or more machine learning algorithms are selected from the group consisting of:

k-nearest neighbors, logistic regression, classification and regression trees, linear discriminant analysis, Gaussian naive Bayes and support vector machines.

21. The non-transitory computer-readable storage medium of claim 19, wherein the computer readable instructions that cause the system to evaluate the two or more machine learning algorithms cause the system to:

apply the two or more algorithms to a first subset of the training dataset;

apply the two or more algorithms to a second subset of training dataset different from the first subset; and

select the algorithm and subset that produces the most accurate results against the validation dataset.