Dynamic row ranking of connected fitness content

Abstract

Systems and methods that facilitate and/or enable a connected fitness platform to dynamically rank and select rows (or groups) of content to display to a user are described. For example, a platform may dynamically rank and present rows of exercise classes to a user of an exercise machine via a home screen or other portal into the content provided by the platform.

What is claimed is: 
     
         1 . A connected fitness system, comprising:
 a content module that is configured to access a database that stores available rows of content to be displayed to a user associated with an exercise machine;   a row ranking module that is configured to:
 determine, for each available row of content to be displayed to the user associated with the exercise machine, a likelihood that the user of the exercise machine selects content from the available row of content; and 
 assign a ranking to the available row based on the determined likelihood for the available row of content; and 
   a presentation module that is configured to present a subset of the available rows of content to the user based on the rankings assigned to the available rows of content.   
     
     
         2 . The connected fitness system of  claim 1 , wherein the row ranking module determines the likelihood that the user of the exercise machine selects content from the available row of content by:
 determine a counterfactual estimate of conversion of the available row of content; and   assign the rankings to the available rows of content based on the counterfactual estimates of conversion.   
     
     
         3 . The connected fitness system of  claim 2 , wherein the row ranking module is further configured to apply a bandit model based on Thompson sampling to the rankings assigned to the available rows of content. 
     
     
         4 . The connected fitness system of  claim 1 , wherein each of the available rows of content are associated with a unique row type. 
     
     
         5 . The connected fitness system of  claim 1 , wherein the presentation module is configured to present a highest ranked row of available content at a top portion of a user interface associated with the exercise machine. 
     
     
         6 . The connected fitness system of  claim 1 , wherein the presentation module presents the subset of the available rows of content to the user based on the rankings assigned to the available rows of content in response to an occurrence of an event at a user interface associated with the exercise machine. 
     
     
         7 . The connected fitness system of  claim 1 , wherein the presentation module presents the subset of the available rows of content to the user based on the rankings assigned to the available rows via a home screen associated with the connected fitness system. 
     
     
         8 . The connected fitness system of  claim 1 , wherein the presentation module presents the subset of the available rows of content to the user based on the rankings assigned to the available rows via a display of the exercise machine. 
     
     
         9 . The connected fitness system of  claim 1 , wherein the presentation module presents the subset of the available rows of content to the user based on the rankings assigned to the available rows via a mobile device associated with the user. 
     
     
         10 . The connected fitness system of  claim 1 , wherein the row ranking module is further configured to generate a new row of content based on two or more available rows of content that are assigned high rankings. 
     
     
         11 . The connected fitness system of  claim 1 , wherein the content within each of the available rows of content include exercise classes selectable by the user and associated with exercise activities performed by the user via the exercise machine. 
     
     
         12 . A method, comprising:
 determining, for each group of multiple different groups of exercise classes selectable by a user of an exercise machine, a likelihood that the user of the exercise machine selects an exercise class from that group of exercise classes;   assigning a ranking to the group of exercise classes based on the determined likelihood; and   presenting the multiple different groups of exercise classes for selection to the user based on the assigned rankings.   
     
     
         13 . The method of  claim 12 , wherein the likelihood of selection for each group of exercise classes is determined by:
 determining a counterfactual estimate of conversion of the group of exercise classes; and   assigning the ranking to the groups of exercise classes based on the counterfactual estimate of conversion.   
     
     
         14 . The method of  claim 13 , further comprising:
 applying a bandit model based on Thompson sampling to the rankings assigned to the groups of exercise classes.   
     
     
         15 . The method of  claim 12 , wherein the multiple different groups of exercise classes include:
 a group of exercise classes associated with instructors previously selected by the user;   a group of exercise classes associated with instructors new to the user;   a group of exercise classes associated with musical types previously selected by the user; and   a group of exercise classes popular across all users of a connected fitness platform associated with the exercise machine.   
     
     
         16 . The method of  claim 12 , wherein presenting the multiple different groups of exercise classes for selection to the user based on the assigned rankings includes presenting a highest ranked group of exercise classes at a top portion of a user interface associated with the exercise machine. 
     
     
         17 . The method of  claim 12 , wherein presenting the multiple different groups of exercise classes for selection to the user based on the assigned rankings includes presenting a highest ranked group of exercise classes within a home screen displayed by a user interface associated with the exercise machine. 
     
     
         18 . A non-transitory computer-readable medium whose contents, when executed by a computing system, cause the computing system to perform a method, the method comprising:
 receiving an indication that a user of an exercise machine has accesses a home screen into a connected fitness platform;   dynamically ranking a list of rows of content available to the user of the exercise machine; and   presenting the rows of content available to the user via the home screen and based on the dynamic ranking of the list of the rows of content.   
     
     
         19 . The non-transitory computer-readable medium of  claim 18 , wherein the rows of content include groupings of exercise content available to the user and associated with exercise activities performed by the user via the exercise machine. 
     
     
         20 . The non-transitory computer-readable medium of  claim 18 , wherein dynamically ranking a list of rows of content available to the user of the exercise machine includes:
 determining counterfactual estimates of conversion for the rows of content;   assigning rankings to the rows of content based on the counterfactual estimate of conversion; and   applying a bandit model based on Thompson sampling to the rankings assigned to the rows of content.