Methods and apparatus to generate recommendations based on attribute vectors

1. An apparatus to generate one or more recommendations, the apparatus comprising:

an attribute vector selector to determine an artist that that is to perform at an event hosted at a venue and a first person that is to attend the event;

an attribute vector aggregator to form an event attribute vector associated with the event based on a weighted average of an attendee attribute vector associated with the first person and an artist attribute vector associated with the artist, the artist attribute vector weighted more heavily than the attendee attribute vector; and

a recommendation generator including:

a vector comparator to perform a comparison between a query attribute vector and the event attribute vector, the query attribute vector associated with a media player of a device associated with a second person; and

a recommendation compiler to:

create a recommendation of at least one of the artist or the event for the second person based on the comparison, wherein creating the recommendation based on the comparison comprises creating the recommendation based on a finding that the query attribute vector is threshold similar to the event attribute vector; and

cause the media player to access a playlist of one or more media samples based on the recommendation, at least one of the attribute vector selector, the attribute vector aggregator, the vector comparator, or the recommendation compiler implemented by processor circuitry.

2. The apparatus of claim 1 , wherein at least one of the event attribute vector, the attendee attribute vector, the artist attribute vector, or the query attribute vector includes a numerical vector, the numerical vector correlated with a set of attributes describing the numerical vector according to corresponding indices of the set of attributes.

3. The apparatus of claim 1 , wherein the attribute vector aggregator is to determine the weighted average of the attendee attribute vector and the artist attribute vector.

4. The apparatus of claim 1 , wherein the vector comparator is to determine a mathematical difference between the query attribute vector and the event attribute vector to perform the comparison between the query attribute vector and the event attribute vector.

5. The apparatus of claim 1 , wherein the event attribute vector is a first event attribute vector, the attendee attribute vector is a first attendee attribute vector, the artist attribute vector is a first artist attribute vector, and the query attribute vector to the recommendation generator includes at least one of a media attribute vector associated with the media player, a second event attribute vector associated with the media player, a second attendee vector associated with the media player, or a second artist attribute vector associated with the media player.

6. The apparatus of claim 1 , wherein the recommendation includes the venue at which the event is hosted.

7. The apparatus of claim 1 , wherein the attribute vector aggregator is to:

generate the artist attribute vector based on a first media attribute vector associated with a recent set list of the artist and a second media attribute vector associated with an older set list of the artist, the first media attribute vector weighted more heavily than the second media attribute vector;

generate the attendee attribute vector based on a third media attribute vector associated with the first person and a fourth media attribute vector associated with the first person; and

generate a venue attribute vector based on the event attribute vector.

8. The apparatus of claim 1 , further including a recommendation provider to provide the recommendation to the device.

9. At least one non-transitory computer readable medium comprising instructions that, when executed, cause at least one processor to at least:

generate an artist attribute vector associated with an artist that is to perform at an event hosted at a venue, the artist attribute vector based on a first media attribute vector associated with a recent set list of the artist and a second media attribute vector associated with an older set list of the artist, the first media attribute vector weighted more heavily than the second media attribute vector;

generate an attendee attribute vector associated with a person that is to attend the event, the attendee attribute vector based on a third media attribute vector associated with the person and a fourth media attribute vector associated with the person;

generate an event attribute vector based on the attendee attribute vector and the artist attribute vector;

generate a venue attribute vector based on the event attribute vector;

generate a query attribute vector based on at least a fifth media attribute vector;

perform a comparison between the query attribute vector and a comparison attribute vector selected from the group consisting of the artist attribute vector, the event attribute vector, and the venue attribute vector;

create a recommendation of at least one of the artist, the event, or the venue based on the comparison, wherein creating the recommendation based on the comparison comprises creating the recommendation based on a finding that the query attribute vector is threshold similar to the comparison attribute vector; and

cause a media player of a device to access a playlist of media samples based on the recommendation.

10. The at least one non-transitory computer readable medium of claim 9 , wherein the person is a first person,

wherein the device is associated with a second person,

wherein the fifth media attribute vector is associated with a media player of the device associated with the second person, and

wherein causing the media player of the device to access the playlist of media samples based on the recommendation comprises providing the playlist of media samples to the device.

11. The at least one non-transitory computer readable medium of claim 10 , wherein the instructions, when executed, cause the at least one processor to generate the playlist of media samples based on the recommendation.

12. The at least one non-transitory computer readable medium of claim 10 , wherein the recommendation includes at least one of a media sample of the playlist of media samples, the artist, the event, or the venue.

13. The at least one non-transitory computer readable medium of claim 9 , wherein the event attribute vector is a first event attribute vector, the event is a first event, and the instructions, when executed, cause the at least one processor to:

generate a second event attribute vector associated with a second event hosted at the venue; and

determine an average of the first event attribute vector and the second event attribute vector to generate the venue attribute vector.

14. A method of generating one or more recommendations, the method comprising:

calculating, by executing one or more instructions with a processor, an artist attribute vector associated with an artist that is to perform at an event hosted at a venue, the artist attribute vector based on a first media attribute vector associated with a recent set list of the artist and a second media attribute vector associated with an older set list of the artist, the first media attribute vector weighted more heavily than the second media attribute vector;

calculating, by executing one or more instructions with the processor, an attendee attribute vector associated with a person that is to attend the event, the attendee attribute vector based on a third media attribute vector associated with the person and a fourth media attribute vector associated with the person;

calculating, by executing one or more instructions with the processor, an event attribute vector based on the attendee attribute vector and the artist attribute vector;

calculating, by executing one or more instructions with the processor, a venue attribute vector based on the event attribute vector;

generating, by executing one or more instructions with the processor, a query attribute vector based on at least a fifth media attribute vector;

performing, by executing one or more instructions with the processor, a comparison between the query attribute vector and a comparison attribute vector selected from the group consisting of the artist attribute vector, the event attribute vector, and the venue attribute vector;

creating, by executing one or more instructions with the processor, a recommendation of at least one of the artist, the event, or the venue based on the comparison, wherein creating the recommendation based on the comparison comprises creating the recommendation based on a finding that the query attribute vector is threshold similar to the comparison attribute vector; and

causing a media player of a device to access a playlist of media samples based on the recommendation.

15. The method of claim 14 , wherein the person is a first person,

wherein the device is associated with a second person,

wherein the fifth media attribute vector is associated with a media player of the device associated with the second person, and

wherein causing the media player of the device to access the playlist of media samples based on the recommendation comprises providing the playlist of media samples to the device.

16. The method of claim 15 , wherein at least one of the query attribute vector, the fifth media attribute vector, the artist attribute vector, the event attribute vector, or the venue attribute vector includes a numerical vector including dimensional indices, and wherein performing the comparison between the query attribute vector and the comparison attribute vector comprises determining an absolute value of a mathematical difference between the query attribute vector and the comparison attribute vector.

17. The method of claim 15 , wherein the artist attribute vector is a first artist attribute vector, the query attribute vector includes a second artist attribute vector associated with a second artist, and the method further includes generating a recommendation of the second artist based on the comparison.

18. The method of claim 14 , wherein the event attribute vector is a first event attribute vector, and the method further includes:

generating, by executing one or more instruction with the processor, a second event attribute vector associated with a second event hosted at the venue; and

generating, by executing one or more instructions with the processor, the venue attribute vector based on an average of the first event attribute vector and the second event attribute vector.