Methods and systems for personalizing content in accordance with divergences in a user's listening history

Patent metadata

Field	Value
Publication number	US-10412183-B2
Application number	US-201715684841-A
Country	US
Kind code	B2
Filing date	Aug 23, 2017
Priority date	Feb 24, 2017
Publication date	Sep 10, 2019
Grant date	Sep 10, 2019

Abstract

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

First claim

What is claimed is:

1. 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 adjusting an order of tracks within a playlist to promote a track within the playlist, the track 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 playlist to the client device of the user in accordance with the adjusted order.

2. The method of claim 1 , wherein the set of characteristics includes genre and mood.

3. The method of claim 1 , 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 the twelve 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.

4. The method of claim 1 , wherein:
the listening contexts include respective time periods during a day; and
the current listening context includes a current time of day.

5. The method of claim 1 , wherein:
the listening contexts include respective days of a week; and
the current listening context includes a current day of the week.

6. The method of claim 1 , wherein:
the listening contexts include locations of the user; and
the current listening context includes a current location of the user.

7. The method of claim 1 , wherein:
the listening contexts include weather; and
the current listening context includes current weather at a current location of the user.

8. The method of claim 1 , wherein:
the listening contexts include playback devices; and
the current listening context includes a current playback device.

9. The method of claim 1 , wherein providing the personalized content comprises forgoing to provide a recommended playlist.

10. The method of claim 9 , wherein forgoing to provide a recommended playlist comprises forgoing to provide a playlist in which a number of tracks having the representative value of the identified characteristic does not satisfy a threshold.

11. The method of claim 1 , 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.

12. The method of claim 11 , wherein the message is associated in the media-providing service with the representative value of the identified characteristic.

13. The method of claim 1 , wherein the calculated divergence comprises a Jensen-Shannon Divergence for the characteristic with respect to the listening contexts.

14. The method of claim 1 , 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.

15. The method of claim 1 , 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.

16. 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 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 adjusting an order of tracks within a playlist to promote a track within the playlist, the track 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 playlist to the client device of the user in accordance with the adjusted order.

17. 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 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 dive

Assignees

• Spotify Ab

Inventors

• Gibson Clay
• Shapiro Will
• Gil Santiago
• Anderson Ian
• Mpossi Margreth
• Semerci Oguz
• Wolf Scott

Classifications

• G06F16/635

  Filtering based on additional data, e.g. user or group profiles · CPC title

• H04L65/4069

  Electricity · mapped topic

• H04L67/18

  Electricity · mapped topic

• H04L67/22 Primary

  Electricity · mapped topic

• G06F16/435 Primary

  Filtering based on additional data, e.g. user or group profiles · CPC title