System and methods for detecting temporal music trends from online services

Patent metadata

Field	Value
Publication number	US-9524487-B1
Application number	US-201213466817-A
Country	US
Kind code	B1
Filing date	May 8, 2012
Priority date	Mar 15, 2012
Publication date	Dec 20, 2016
Grant date	Dec 20, 2016

Abstract

A system and methods for automatically detecting temporal music trends by observing music consumption by users of online services, for example, social networks, and user sharing habits. In some embodiments, the system and methods gather music consumption patterns (e.g., downloading, listening, sharing or the like) of users, including music identifiers for a track, album, or playlist in a user's music library and time stamps that indicate consumption times corresponding to the music identifiers. A temporal trends detection engine determines music of interest to users by analyzing music consumption patterns of users, user interests and tastes in music, and social affinity between users. A recommendations engine automatically generates and transmits recommendations of music determined by the temporal trends detection engine to be of interest to users.

First claim

What is claimed is:

1. A computer-implemented method for detecting temporal music trends in an online community, executing on one or more computing devices, the method comprising:
identifying a type of music for a particular user based on activities of the particular user inside and outside the online community;
determining, by at least one of the one or more computing devices, users of the online community that share a common interest in the type of music with the particular user;
obtaining, by at least one of the one or more computing devices, music consumption data for the users that share the common interest in the type of music with the particular user, wherein the music consumption data includes a plurality of items of music having the type of music, and timestamps that indicate when and how many times each one of the plurality of items of music was consumed by the users, the plurality of items of music identified by unique music identifiers;
compiling a list of the plurality of items of music consumed by the users that share the common interest in the type of music with the particular user, the list of the plurality of items of music including the time stamps that indicate when and how many times each of the plurality of items of music was consumed by the users;
determining, from the list, one or more popular items of music that are popular based on the time stamps that indicate when and how many times the one or more popular items of music were consumed by the users;
determining a strength of a social affinity between the users and the particular user based on interactions between the users and the particular user in the online community;
generating recommendations, by at least one of the one or more computing devices, for the particular user, including transmitting the recommendations for the particular user from the users that share the common interest in the type of music with the particular user based on the strength of the social affinity between the particular user and the users, and reconfiguring the recommendations for display to the particular user by determining real-time popular items that are popular based on time stamps; and
providing for display to the particular user the generated recommendations.

2. The computer-implemented method of claim 1 , further comprising:
formulating an assessment code index with a coding scheme to designate different types of relationships between users of the online community including the particular user.

3. The computer-implemented method of claim 2 , further comprising:
determining the strength of the social affinity, based at least in part, on the assessment code index, wherein the assessment code index has different designations for different types of relationships.

4. The computer-implemented method of claim 2 , further comprising:
formulating a binary code to designate if the recommendations should be transmitted from other users of the online community to the particular user; and
generating the strength of the social affinity, based at least in part, on the assessment code index, wherein the assessment code index designates a first code to designate sharing between users that share the social affinity with the particular user and a second code to designate no sharing between users that do not share the social affinity with the particular user.

5. A system for detecting temporal music trends in an online community, comprising one or more computing devices, the system comprising:
a social affinity designation engine for identifying a type of music for a particular user based on activities of the particular user inside and outside the online community, determining other users of the online community that share a common interest in the type of music with the particular user, and determining a strength of a social affinity that the other users share with the particular user based on interactions between the other users and the particular user in the online community;
a temporal trends detection engine for obtaining music consumption data for the particular user, wherein the music consumption data includes a plurality of items of music having the type of music, and timestamps that indicate when and how many times each one of the plurality of items of music was consumed by the particular user, the plurality of items of music identified by unique music identifiers;
a temporal filter engine for compiling a list of the plurality of items of music consumed by the particular user, the list of the plurality of items of music including the time stamps; and
a current trends recommendation engine for determining, from the music consumption data, one or more popular items of music that are popular based on the time stamps that indicate when and how many times the one or more popular items of music were consumed by the particular user, and for generating recommendations to share the one or more popular items of music, based at least in part, on the strength of the social affinity between the particular user and the other users within the online community.

6. The system of claim 5 , wherein the current trends recommendation engine is configured for transmitting the recommendations from the particular user to the other users, based at least in part, on determining the strength of the social affinity between the other users and the particular user, and is configured to reconfigure the recommendations for display to the particular user by determining real-time popular items that are popular based on the time stamps.

7. The system of claim 5 , wherein the social affinity designation engine formulates an assessment code index with a coding scheme to designate different types of relationships between the other users of the online community and the particular user.

8. The system of claim 7 , wherein the social affinity designation engine determines the strength of the social affinity, based at least in part, on the assessment code index, wherein the assessment code index has different designations for different types of relationships.

9. The system of claim 7 , wherein the social affinity designation engine formulates a binary code to designate if the recommendations should be transmitted from the particular user to the other users of the online community and generates the strength of the social affinity, based at least in part, on the assessment code index, wherein the assessment code index designates a first code to designate sharing between the particular user and the other users that share the social affinity with the particular user and a second code to designate no sharing between the other users that do not share the social affinity with the particular user.

10. A system for generating recommendations of music for an online service, comprising:
a social affinity engine for identifying a type of music for a particular user based on activities of the particular user inside and outside the online service, determining other users of the online service that share a common interest in the type of music with the particular user, and determining a strength of a social affinity between the other users of the online service with the particular user based on interactions between the particular user and the other users of the online service;
a detection engine for compiling music consumption data for the particular user of the online service, by using a user name to obtain one or more features from the music consumption data for the particular user, wherein the music consumption data includes a plurality of items of music having the type of music, and timestamps that indicate when and how many times an item of music from the plurality of items of music was consumed by the particular u

Assignees

• Yagnik Jay
• Eck Douglas
• Google Inc

Inventors

• Yagnik Jay
• Eck Douglas

Classifications

• G06F16/00

  Information retrieval; Database structures therefor; File system structures therefor · CPC title

• G06Q30/00

  Commerce · CPC title

• G06Q10/10 Primary

  Office automation; Time management · CPC title

• G06F17/30

  Physics · mapped topic