Event recommendations using machine learning
OwnerRavish Chawla, Rohit Pradeep Shetty +1inventors
InventorsRAVISH CHAWLA, Rohit Pradeep Shetty, Adam Chow
Life of this patent
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Abstract
Disclosed herein are examples of systems and methods for recommending events using machine learning. A first recommendation can be generated based at least in part on at least one user parameter associated with a user. The first recommendation can comprise a first event. The first recommendation can be provided to a client device associated with the user, and a user response to the recommendation can be received from the client device. A second recommendation can be generated based at least in part on the user response and the at least one user parameter, wherein the second recommendation comprises a second event. The second recommendation can be provided to the client device.
Claims20
A system for recommending events using machine learning, comprising:
- at least one computing device comprising a processor and a memory;
- machine-readable instructions stored in the memory that, when executed by the processor, cause the at least one computing device to at least:
- generate a first recommendation based at least in part on at least one user parameter associated with a user, the first recommendation comprising a first event;
- provide the first recommendation to a client device associated with the user;
- receive a user response to the first recommendation from the client device;
- generate a second recommendation based at least in part on the user response and the at least one user parameter, the second recommendation comprising a second event;
- and
- provide the second recommendation to the client device.
A method for recommending events using machine learning, comprising:
- generating a first recommendation based at least in part on at least one user parameter associated with a user, the recommendation comprising a first event;
- providing the first recommendation to a client device associated with the user;
- receiving a user response to the first recommendation;
- generating a second recommendation based at least in part on the user response and the at least one user parameter, the second recommendation comprising a second event;
- and
- providing the second recommendation to the client device.
A non-transitory, computer-readable medium for recommending events using machine learning, comprising machine-readable instructions that, when executed by a processor of at least computing device, cause the processor to at least:
- generate a first recommendation based at least in part on at least one user parameter associated with a user, the recommendation comprising a first event;
- provide the first recommendation to a client device associated with the user;
- receive a user response to the first recommendation from the client device;
- generate a second recommendation based at least in part on the user response and the at least one user parameter, the second recommendation comprising a second event;
- and
- provide the second recommendation to the client device.
Dependencies are inferred from the claim text.