Application Complexity Computation

• US 20160132771A1
• Filed: 11/12/2014
• Published: 05/12/2016
• Est. Priority Date: 11/12/2014
• Status: Abandoned Application

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Shetty, Sanketh, Elbouchikhi, Ibrahim
• Application Number:
  US14/539,392
• Publication Number:
  US 20160132771A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: H04L 67/60 Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources admission control or resource allocation H04L47/70
• CPC Class Codes: H04W 4/60 Subscription-based services using application servers or record carriers, e.g. SIM application toolkits

Abstract

A machine learning technique may be applied to applications hosted by an application store to extract features that can be utilized to train one or more classifiers of the applications based on their relative complexity. A processor may receive pairwise comparisons of relative complexity and feature representations for the applications to be used in training of a classifier. The processor may determine a feature set that is correlated with the pairwise comparison of relative complexity and obtain a classifier based thereupon.

Claims

1. A computer-implemented method, comprising:

receiving, by a processor, a plurality of pairwise comparisons of relative complexity for each one of a plurality of applications as compared to an other of the plurality of applications, wherein the plurality of applications are hosted on an application store server;

obtaining a plurality of features for the plurality of applications, wherein the plurality of features comprises at least one of;

a visual density of an image, a frequency of scene changes in a video, an average length of failed gameplay, a rating, a download number, and a sentiment metric; and

obtaining a classifier by determining, by the processor, a feature set comprising a portion of the plurality of features that correspond to a plurality of features that are correlated with the pairwise comparison of relative complexity for the plurality of applications.

2. The method of claim 1, further comprising selecting the plurality of applications based on an application category.

3. The method of claim 1, further comprising:

receiving a new application from a client device;

applying the classifier to a plurality of images for the new application; and

generating a complexity score for the new application.

4. The method of claim 3, further comprising presenting the complexity score to a second client device connected to the application store.

5. The method of claim 3, further comprising:

receiving, by the application store, a query from a second client device associated with a user; and

responsive to the query, generating a recommendation based on the query, the user of the second client device, and the complexity score.

6. The method of claim 5, further comprising determining a user expertise level based on at least one of:

demographic information for the user and a success ratio for the user, wherein the recommendation is further based on the user expertise level.

7. The method of claim 1, wherein one of the portion of the plurality of features comprises a user expertise level that is based on at least one of demographic information for a plurality of users and a success ratio for the plurality of users.

8. The method of claim 1, wherein obtaining the classifier comprises training the classifier on a portion of the plurality of applications, wherein the portion of the plurality of applications are complex based on the pairwise comparisons of relative complexity.

9. A system, comprising:

a database for storing a plurality of pairwise comparisons of relative complexity for each one of a plurality of applications as compared to an other of the plurality of applications, wherein the plurality of applications are hosted by an application store server;

a processor communicatively coupled to the database, the processor configured to;

receive the plurality of pairwise comparisons of relative complexity;

obtain a plurality of features for the plurality of applications, wherein the plurality of features comprises at least one of;

a visual density of an image, a frequency of scene changes in a video, an average length of failed gameplay, a rating, a download number, and a sentiment metric; and

obtain a classifier by determining a feature set comprising a portion of the plurality of features that correspond to a plurality of features that are correlated with the pairwise comparison of relative complexity for the plurality of applications.

10. The system of claim 9, the processor further configured to select the plurality of applications based on an application category.

11. The system of claim 9, the processor further configured to:

receive a new application from a client device;

apply the classifier to a plurality of images for the new application; and

generate a complexity score for the new application.

12. The system of claim 11, the processor further configured to present the complexity score to a second client device connected to the application store.

13. The system of claim 11, the processor further configured to:

receive, by the application store, a query from a second client device associated with a user; and

responsive to the query, generate a recommendation based on the query, the user of the second client device, and the complexity score.

14. The system of claim 13, further comprising determining a user expertise level based on at least one of:

demographic information for a user and a success ratio for the user, wherein the recommendation is further based on the user expertise level.

15. The system of claim 9, wherein one of the portion of the plurality of features comprises a user expertise level that is based on at least one of demographic information for a plurality of users and a success ratio for the plurality of users.

16. The system of claim 9, wherein obtaining the classifier comprises training the classifier on a portion of the plurality of applications, wherein the portion of the plurality of applications are complex based on the pairwise comparisons of relative complexity.