System and methods for detecting temporal music trends from online services

• US 9,524,487 B1
• Filed: 05/08/2012
• Issued: 12/20/2016
• Est. Priority Date: 03/15/2012
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Yagnik, Jay, Eck, Douglas
• Primary Examiner(s): Homayounmehr, Farid
• Assistant Examiner(s): Sarker, Sanchit
• Application Number:
  US13/466,817
• Time in Patent Office: 1,687 Days
• Field of Search: 709/204
• US Class Current: 1/1
• CPC Class Codes: G06F 16/00 Information retrieval; Database structures therefor; File system structures therefor
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 30/00 Commerce

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.

Claims

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 user, each of the plurality of items of music having a unique music identifier;

a predictor engine for retrieving the music consumption data including the unique music identifiers and the time stamps that indicate when and how many times an item of music was consumed by the particular user to determine, from the music consumption data, a popular item of music that is popular; and

a recommendation engine for automatically generating one or more recommendations of music from the particular user for the other users of the online service based on the popular item of music and the strength of the social affinity between the particular user and the other users.

11. A computer program product comprising a non-transitory computer useable medium including a computer readable program, wherein the computer readable program when executed on a computer causes the computer to:

identify, by one or more computing devices within the computer, a type of music for a particular user based on activities of the particular user inside and outside an online service;

determine, by the one or more computing devices within the computer, other users of the online service that share a common interest in the type of music with the particular user;

compile, by the one or more computing devices within the computer, in a local storage, music consumption data for the particular user of the online service, wherein the music consumption data includes one or more items of music having the type of music, and timestamps that indicate when and how many times each of the one or more items of music was consumed by the particular user, each of the one or more items of music having a unique music identifier;

retrieve the music consumption data including unique music identifiers and the time stamps that indicate when and how many times each of the one or more items of music was consumed by the particular user;

determine, from the music consumption data, one or more popular items of music that meet a designated criteria, the criteria including a designation of times at which the one or more popular items of music were consumed, and meeting the designation of times indicating that the one or more popular items are popular;

determine, by the one or more computing devices within the computer, a strength of a social affinity between the particular user and the other users based on interactions between the particular user and the other users of the online service; and

generate one or more recommendations from the particular user to other users within the online community based on the one or more popular items of music and the strength of the social affinity between the other users of the online service and the particular user;

transmit the one or more recommendations for display from the particular user to the other users based on the strength of the social affinity between them;

reconfigure the recommendations for display to the particular user by determining real-time popular items that are popular based on the time stamps; and

provide for display to the particular user the reconfigured recommendations.

12. The computer-program product of claim 11, further comprising:

formulating an assessment code to designate varying types of relationships between the particular user and the other users of the online service; and

generating the strength of the social affinity, based at least in part, on the assessment code wherein the assessment code has different designations for different types of relationships.

13. A computer-implemented method for generating a user interface comprising:

identifying, by at least one of one or more computing devices, a type of music for a particular user based on activities of the particular user inside and outside an online service;

determining, by at least one of the one or more computing devices, other users of the online service that share a common interest in the type of music with the particular user;

compiling, by at least one of the one or more computing devices, in a local storage, music consumption data for the particular user of the online service, wherein the music consumption data includes one or more items of music having the type of music, and timestamps that indicate when and how many times each of the one or more items of music was consumed by the particular user, each of the one or more items of music having a unique music identifier;

retrieving the music consumption data including unique music identifiers and the time stamps that indicate when and how many times each of the one or more items of music was consumed by the particular user;

determining, from the music consumption data, one or more popular items of music that meet a designated criteria, the criteria including a designation of times at which the one or more popular items of music were consumed, and meeting the designation of times indicating that the one or more popular items are popular;

determining, by at least one of the one or more computing devices, a strength of a social affinity between the particular user and the other users based on interactions between the particular user and the other users of the online service;

generating recommendations to the other users of the online service based on the one or more popular items of music and the strength of the social affinity between the other users of the online service and the particular user;

transmitting the recommendations for the particular user from the other users that share the common interest in the type of music with the particular user, based on determining the strength of the social affinity between the users and the particular user;

reconfiguring the recommendations for display to the particular user by determining real-time popular items that are popular based on the time stamps; and

providing for display to the particular user the reconfigured recommendations.

14. A computer-implemented method for generating a user interface according to claim 13, wherein the strength of the social affinity between the particular user and the other users is determined based on an assessment code that is formulated to designate varying types of relationships between the particular user and the other users.

15. A computer-implemented method for generating a user interface according to claim 13, wherein the timestamps indicate at least one of a time of day, week, month, or year.