Methods and Systems for Personalizing Content in Accordance with Divergences in a User's Listening History

Abstract

An electronic device accesses a listening history of a user of a media-providing service. For each characteristic of a set of characteristics describing tracks provided by the media-providing service, the electronic device determines, for the user, a measure of divergence indicating a degree of lack of uniformity in the listening history of the user with respect to listening contexts. The electronic device identifies a characteristic, of the set of characteristics, having a respective measure of divergence that satisfies a threshold. The electronic device determines a representative value of the identified characteristic for a listening context corresponding to a current listening context of the user and provides personalized content to a client device of the user based on the representative value of the identified characteristic, including providing a recommended playlist that includes tracks having the representative value of the identified characteristic for the listening context corresponding to the current listening context.

1 . (canceled) 
     
     
         2 . A method, comprising:
 at an electronic device associated with a media-providing service and having one or more processors and memory storing instructions for execution by the one or more processors:
 accessing a listening history of a user of the media-providing service; 
 for each characteristic of a set of characteristics describing tracks provided by the media-providing service, determining, for the user, a measure of divergence indicating a degree of lack of uniformity in the listening history of the user with respect to listening contexts, the determining including normalizing, based on the listening history, a calculated divergence for the characteristic relative to measures of divergence of other users in the listening contexts, wherein each characteristic has a plurality of possible values; 
 identifying a characteristic, of the set of characteristics, having a respective measure of divergence that satisfies a threshold indicative of the lack of uniformity in the listening history with respect to the listening contexts; 
 determining a representative value of the identified characteristic for a listening context corresponding to a current listening context of the user; 
 in the current listening context, providing personalized content to a client device of the user based on the representative value of the identified characteristic, including providing a recommended playlist that includes tracks having the representative value of the identified characteristic for the listening context corresponding to the current listening context; and 
 streaming one or more tracks within the recommended playlist to the client device of the user. 
   
     
     
         3 . The method of  claim 2 , wherein the set of characteristics includes genre and mood. 
     
     
         4 . The method of  claim 2 , wherein the set of characteristics includes one or more characteristics selected from the group consisting of a degree to which a track is acoustic, a strength of a beat, a degree to which a track sounds choppy, suitability for dancing, positions of a track where downbeats occur, duration of a track, average dynamic range, a measure of energy of a track, a degree to which pitches are balanced, a degree to which a track contains vocals, key, a degree to which a live audience is present, loudness, a degree of consistency of timing, fade-in point, fade-out point, modal scale, suitability for running, a degree to which a track includes spoken words, tempo, time signature, and a degree to which a track is cheerful. 
     
     
         5 . The method of  claim 2 , wherein:
 the listening contexts include respective time periods during a day; and   the current listening context includes a current time of day.   
     
     
         6 . The method of  claim 2 , wherein:
 the listening contexts include respective days of a week; and   the current listening context includes a current day of the week.   
     
     
         7 . The method of  claim 2 , wherein:
 the listening contexts include locations of the user; and   the current listening context includes a current location of the user.   
     
     
         8 . The method of  claim 2 , wherein:
 the listening contexts include weather; and   the current listening context includes current weather at a current location of the user.   
     
     
         9 . The method of  claim 2 , wherein:
 the listening contexts include playback devices; and   the current listening context includes a current playback device.   
     
     
         10 . The method of  claim 2 , wherein providing the personalized content comprises adjusting an order of tracks in the recommended playlist based on the representative value of the identified characteristic. 
     
     
         11 . The method of  claim 10 , wherein adjusting the order of tracks in the recommended playlist comprises promoting a track having the representative value of the identified characteristic. 
     
     
         12 . The method of  claim 2 , wherein providing the personalized content further comprises forgoing providing a second recommended playlist. 
     
     
         13 . The method of  claim 12 , wherein forgoing providing a second recommended playlist comprises forgoing providing a playlist in which a number of tracks having the representative value of the identified characteristic does not satisfy a threshold. 
     
     
         14 . The method of  claim 2 , wherein providing the personalized content comprises:
 selecting a message for the user based on the representative value of the identified characteristic; and   providing the selected message to the client device of the user.   
     
     
         15 . The method of  claim 14 , wherein the message is associated in the media-providing service with the representative value of the identified characteristic. 
     
     
         16 . The method of  claim 2 , wherein determining the measure of divergence for each characteristic comprises calculating a Jenson-Shannon divergence for the characteristic with respect to the listening contexts. 
     
     
         17 . The method of  claim 2 , wherein identifying the characteristic comprises:
 ranking characteristics of the set of characteristics that have measures of divergence that satisfy the threshold by the measures of divergence; and   selecting the characteristic with the greatest measure of divergence as the identified characteristic.   
     
     
         18 . The method of  claim 2 , wherein determining the representative value of the identified characteristic for the listening context corresponding to the current listening context of the user comprises:
 determining, for the listening context corresponding to the current listening context of the user, the mode of the plurality of possible values of the identified characteristic in the listening history of the user; and   assigning the mode as the representative value.   
     
     
         19 . An electronic device, comprising:
 one or more processors; and   memory storing one or more programs for execution by the one or more processors, the one or more programs including instructions for:
 accessing a listening history of a user of a media-providing service; 
 for each characteristic of a set of characteristics describing tracks provided by the media-providing service, determining, for the user, a measure of divergence indicating a degree of lack of uniformity in the listening history of the user with respect to listening contexts, the determining including normalizing, based on the listening history, a calculated divergence for the characteristic relative to measures of divergence of other users in the listening contexts, wherein each characteristic has a plurality of possible values; 
 identifying a characteristic, of the set of characteristics, having a respective measure of divergence that satisfies a threshold indicative of the lack of uniformity in the listening history with respect to the listening contexts; 
 determining a representative value of the identified characteristic for a listening context corresponding to a current listening context of the user; 
 in the current listening context, providing personalized content to a client device of the user based on the representative value of the identified characteristic, including providing a recommended playlist that includes tracks having the representative value of the identified characteristic for the listening context corresponding to the current listening context; and 
 streaming one or more tracks within the recommended playlist to the client device of the user. 
   
     
     
         20 . A non-transitory computer-readable storage medium, storing one or more programs for execution by one or more processors of an electronic device, the one or more programs including instructions for:
 accessing a listening history of a user of a media-providing service;   for each characteristic of a set of characteristics describing tracks provided by the media-providing service, determining, for the user, a measure of divergence indicating a degree of lack of uniformity in the listening history of the user with respect to listening contexts, the determining including normalizing, based on the listening history, a calculated divergence for the characteristic relative to measures of divergence of other users in the listening contexts, wherein each characteristic has a plurality of possible values;   identifying a characteristic, of the set of characteristics, having a respective measure of divergence that satisfies a threshold indicative of the lack of uniformity in the listening history with respect to the listening contexts;   determining a representative value of the identified characteristic for a listening context corresponding to a current listening context of the user;   in the current listening context, providing personalized content to a client device of the user based on the representative value of the identified characteristic, including providing a recommended playlist that includes tracks having the representative value of the identified characteristic for the listening context corresponding to the current listening context; and   streaming one or more tracks within the recommended playlist to the client device of the user.