User profile based on clustering tiered descriptors
Methods, apparatus, systems, and articles of manufacture are disclosed that involve a user profile based on clustering tiered descriptors. An example method includes grouping descriptors into a cluster of descriptors based on an association between the descriptors and each of a first item and a second item, accessing, via a user device, biometric data of a user, determining a first activity in which the user is engaged based on contextual data that correlates the cluster of descriptors with the biometric data of the user received from the user device via the network, generating a user profile based on the first activity of the user and the cluster of descriptors, and generating, in response to a second activity of the user matching the first activity associated with the cluster of descriptors within the user profile, a recommendation including a third item based on the user profile.
1. An apparatus comprising:
memory; and
at least one processor to execute instructions to at least:
group descriptors into a cluster of descriptors based on an association between the descriptors and each of a first item and a second item;
access biometric data of a user via a user device;
determine a first activity in which the user is engaged based on contextual data that correlates the cluster of descriptors with the biometric data of the user received from the user device via the network;
determine whether the first activity represents an anomalous phase of the user based on a time period of the first activity having a duration shorter than a threshold duration;
in response to the determining that the first activity does not represent the anomalous phase of the user, generate a user profile based on the first activity of the user and the cluster of descriptors;
in response to the determining that the first activity represents the anomalous phase of the user, omit a name of the cluster descriptors from the user profile to reduce size of the profile; and
in response to a second activity of the user matching the first activity associated with the cluster of descriptors within the user profile, generate a recommendation including a third item based on the user profile.
2. The apparatus of claim 1 , wherein:
the first item and the second item are included in a collection of items associated with the user;
the collection of items is a media library of the user;
the first item is a first media file in the media library of the user; and
the second item is a second media file in the media library of the user.
3. The apparatus of claim 1 , wherein the at least one processor is to:
determine the first activity of the user based on contextual data that correlates the first item and the second item with a first and second location of the user, the locations received from the user device via the network; and
store a name of the group within the user profile as corresponding to the first activity determined based on the first and second location of the user.
4. The apparatus of claim 3 , wherein the at least one processor is to:
when the first location is associated with a neighborhood associated with the user and the second location is associated with a business district, determine the first activity of the user is commuting; and
store the name of the group within the user profile as corresponding to commuting.
5. The apparatus of claim 1 , wherein the at least one processor is to:
determine the first activity of the user based on contextual data that correlates the first item and the second item with a day of week and a time of day; and
store a name of the group within the user profile as corresponding to the first activity determined based on the day of week and the time of day.
6. The apparatus of claim 1 , wherein the at least one processor is to:
determine the first activity of the user based on contextual data that correlates the first item and the second item with an appointment of the user, the appointment retrieved from calendar data associated with the user; and
store a name of the group within the user profile as corresponding to the first activity determined based on the appointment.
7. A non-transitory machine-readable storage medium comprising instructions that, when executed, cause at least one processor to at least:
group descriptors into a cluster of descriptors based on an association between the descriptors and each of a first item and a second item;
access, via a user device communicatively coupled to the machine via a network, biometric data of a user;
determine a first activity in which the user is engaged based on contextual data that correlates the cluster of descriptors with the biometric data of the user received from the user device via the network;
determine whether the first activity represents an anomalous phase of the user based on a time period of the first activity having a duration shorter than a threshold duration;
in response to determining that the first activity does not represent the anomalous phase of the user, generate a user profile based on the first activity of the user and the cluster of descriptors;
in response to determining that the first activity represents the anomalous phase of the user, reduce, by executing an instruction with a processor, the size of user profile by omitting a name of the cluster of descriptors from the user profile; and
when a second activity of the user matches the first activity associated with the cluster of descriptors within the user profile, generate a recommendation including a third item based on the user profile.
8. The non-transitory machine-readable storage medium of claim 7 , wherein:
the first item and the second item are specimens of a collection of items that belong to the user;
the collection of items is a media library of the user;
the first item is a first media file in the media library of the user; and
the second item is a second media file in the media library of the user.
9. The non-transitory machine-readable storage medium of claim 7 , wherein the instructions, when executed, further cause the at least one processor to:
determine the first activity of the user based on contextual data that correlates the first item and the second item with a day of week and a time of day; and
store a name of the group within the user profile as corresponding to the first activity determined based on the day of week and the time of day.
10. The non-transitory machine-readable storage medium of claim 7 , wherein the instructions, when executed, further cause the at least one processor to:
determine the first activity of the user based on contextual data that correlates the first item and the second item with a first and second location of the user, the locations received from the user device via the network; and
store a name of the group within the user profile as corresponding to the first activity determined based on the first and second location of the user.
11. The non-transitory machine-readable storage medium of claim 7 , wherein the instructions, when executed, further cause the at least one processor to:
determine the first activity of the user based on contextual data that correlates the first item and the second item with an appointment of the user, the appointment retrieved from calendar data associated with the user; and
store a name of the group within the user profile as corresponding to the first activity determined based on the appointment.
12. A method comprising:
grouping, by executing an instruction with a processor, descriptors into a cluster of descriptors based on an association between the descriptors and each of a first item and a second item;
accessing, via a user device communicatively coupled to the processor via a network, biometric data of a user;
determining, by executing an instruction the processor, a first activity in which the user is engaged based on contextual data that correlates the cluster of descriptors with the biometric data of the user received from the user device via the network, determining whether the first activity represents an anomalous phase of the user based on a time period of the first activity having a duration shorter than a threshold duration;
in response to determining that the first activity does not represent the anomalous phase of the user, generating, by executing an instruction the processor, a user profile based on the first activity of the user and the cluster of descriptors;
in response to determining that the first activity represents the anomalous phase of the user, reducing, by executing an instruction with a processor, the size of user profile by omitting a name of the cluster of descriptors from the user profile; and
generating, by executing an instruction the processor, in response to a second activity of the user matching the first activity associated with the cluster of descriptors within the user profile, a recommendation including a third item based on the user profile.
13. The method of claim 12 , wherein:
the first item and the second item are included in a collection of items associated with the user;
the collection of items is a media library of the user;
the first item is a first media file in the media library of the user; and
the second item is a second media file in the media library of the user.
14. The method of claim 12 , further including:
determining the first activity of the user based on contextual data that correlates the first item and the second item with a first and second location of the user, the locations received from the user device via the network; and
storing a name of the group within the user profile as corresponding to the first activity determined based on the first and second location of the user.
15. The method of claim 14 , further including:
determining the first activity of the user is commuting in response to determining the first location is associated with a neighborhood associated with the user and the second location is associated with a business district; and
storing the name of the group within the user profile as corresponding to commuting.
16. The method of claim 12 , further including:
determining the first activity of the user based on contextual data that correlates the first item and the second item with a day of week and a time of day; and
storing a name of the group within the user profile as corresponding to the first activity determined based on the day of week and the time of day.
17. The method of claim 12 , further including:
determining the first activity of the user based on contextual data that correlates the first item and the second item with an appointment of the user, the appointment retrieved from calendar data associated with the user; and
storing a name of the group within the user profile as corresponding to the first activity determined based on the appointment.