Systems and methods for determining navigation patterns associated with a social networking system

Abstract

Systems, methods, and non-transitory computer readable media can obtain user navigation data associated with transitions by users between one or more pages associated with a system. Reduced dimensionality user navigation data can be generated based on the user navigation data. A plurality of clusters can be generated based on the reduced dimensionality user navigation data, wherein each cluster of the plurality of clusters corresponds to a user navigation pattern associated with the system.

1 . A computer-implemented method comprising:
 monitoring, by a computing system, transitions of users between pages of a system;   maintaining, by the computing system, a table of the transitions of the users, wherein the table includes a user ID, an origin page, a destination page, a number of counts for a transition from the origin page to the destination page, and a frequency of the transition; and   determining, by the computing system, user navigation data based on the table.   
     
     
         2 . The method of  claim 1 , further comprising:
 determining, by the computing system, frequency vectors for the users based on the user navigation data, wherein the frequency vectors include frequencies for all types of transitions of the users.   
     
     
         3 . The method of  claim 2 , further comprising:
 generating, by the computing system, a frequency vector matrix based on the frequency vectors for the users; and   generating, by the computing system, at least one of: a reduced user vector matrix, an eigenvalue matrix, or an eigenvector matrix based on singular value decomposition of the frequency vector matrix.   
     
     
         4 . The method of  claim 3 , further comprising:
 determining, by the computing system, a cluster of the users based on the reduced user vector matrix, wherein the cluster of the users corresponds with a particular user navigation pattern.   
     
     
         5 . The method of  claim 4 , further comprising:
 determining, by the computing system, the particular user navigation pattern based on a centroid of the cluster multiplied by eigenvectors associated with the eigenvector matrix and eigenvalues associated with the eigenvalue matrix.   
     
     
         6 . The method of  claim 3 , further comprising:
 determining, by the computing system, a number of clusters based on a number of possible types of transitions.   
     
     
         7 . The method of  claim 1 , further comprising:
 normalizing, by the computing system, numbers of counts for the transitions of the users such that a sum of normalized transition counts for a user is one.   
     
     
         8 . The method of  claim 1 , wherein the frequency of the transition is determined based on a fraction of a count of a type of transition from the origin page to the destination page over a count of all types of transitions. 
     
     
         9 . The method of  claim 1 , wherein the pages of the system include an off application state associated with an off state of an application on which the pages are provided. 
     
     
         10 . The method of  claim 1 , wherein the transitions of users are based on a selection of a menu item or a performance of a touch gesture 
     
     
         11 . A computing system comprising:
 at least one processor; and   a memory storing instructions that, when executed by the at least one processor, cause the computing system to perform:
 monitoring transitions of users between pages of a system; 
 maintaining a table of the transitions of the users, wherein the table includes a user ID, an origin page, a destination page, a number of counts for a transition from the origin page to the destination page, and a frequency of the transition; and 
 determining user navigation data based on the table. 
   
     
     
         12 . The computing system of  claim 11 , wherein the instructions further cause the computing system to perform:
 determining, by the computing system, frequency vectors for the users based on the user navigation data, wherein the frequency vectors include frequencies for all types of transitions of the users.   
     
     
         13 . The computing system of  claim 12 , wherein the instructions further cause the computing system to perform:
 generating, by the computing system, a frequency vector matrix that includes the frequency vectors for the users; and   generating, by the computing system, at least one of: a reduced user vector matrix, an eigenvalue matrix, or an eigenvector matrix based on singular value decomposition of the frequency vector matrix.   
     
     
         14 . The computing system of  claim 13 , wherein the instructions further cause the computing system to perform:
 determining, by the computing system, a cluster of the users based on the reduced user vector matrix, wherein the cluster of the users corresponds with a particular user navigation pattern.   
     
     
         15 . The computing system of  claim 14 , wherein the instructions further cause the computing system to perform:
 determining, by the computing system, the particular user navigation pattern based on a centroid of the cluster multiplied by eigenvectors associated with the eigenvector matrix and eigenvalues associated with the eigenvalue matrix.   
     
     
         16 . A non-transitory computer-readable storage medium including instructions that, when executed by at least on processor of a computing system, cause the computing system to perform:
 monitoring transitions of users between pages of a system;   maintaining a table of the transitions of the users, wherein the table includes a user ID, an origin page, a destination page, a number of counts for a transition from the origin page to the destination page, and a frequency of the transition; and   determining user navigation data based on the table.   
     
     
         17 . The non-transitory computer-readable storage medium of  claim 16 , wherein the instructions further cause the computing system to perform:
 determining, by the computing system, frequency vectors for the users based on the user navigation data, wherein the frequency vectors include frequencies for all types of transitions of the users.   
     
     
         18 . The non-transitory computer-readable storage medium of  claim 17 , wherein the instructions further cause the computing system to perform:
 generating, by the computing system, a frequency vector matrix that includes the frequency vectors for the users; and   generating, by the computing system, at least one of: a reduced user vector matrix, an eigenvalue matrix, or an eigenvector matrix based on singular value decomposition of the frequency vector matrix.   
     
     
         19 . The non-transitory computer-readable storage medium of  claim 18 , wherein the instructions further cause the computing system to perform:
 determining, by the computing system, a cluster of the users based on the reduced user vector matrix, wherein the cluster of the users corresponds with a particular user navigation pattern.   
     
     
         20 . The non-transitory computer-readable storage medium of  claim 19 , wherein the instructions further cause the computing system to perform:
 determining, by the computing system, the particular user navigation pattern based on a centroid of the cluster multiplied by eigenvectors associated with the eigenvector matrix and eigenvalues associated with the eigenvalue matrix.