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US 12,591,862 B2Granted
System and method for providing pairings for live digital interactions
AssigneeTinder, Inc.
InventorsKyle Garrett Miller, Joshua David Gafni, Danielle Mariah Zegelstein
Life of this patent
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Abstract
A system includes a processor and an interface coupled to the processor. The processor determines that a first user takes a first action, and determines to pair the first user and a second user. The processor pairs the first user and the second user for an interaction, where the interaction lasts for a first time period. The interface receives an indication of a preference of the first user for the second user at a second time. The second time is after the first time period.
Claims51
A system comprising an interaction-based recommendation tool comprising:
- a processor configured to:
- determine that a first user takes a first action with respect to an interaction-based recommendation tool;
- extract a first set of features from a preference history of the first user, the first set of features indicating preferences of the first user with respect to other users;
- extract a second set of features from a preference history of a second user, the second set of features indicating preferences of the second user with respect to other users;
- determine to pair the first user and the second user based at least on the first action, the first set of features, and the second set of features;
- pair the first user and the second user for an interaction, the interaction lasting for a first time period, the interaction is concurrent for the first user and the second user;
- and
- determine an interaction history related to the interaction;
- an interface coupled to the processor, the interface configured to receive an indication of a preference of the first user for the second user at a second time, the second time being after the first time period, the indication is one of user ratings;
- the processor is further configured to:
- extract a machine learning feature from the indication, the machine learning feature indicating a compatibility score between the first user and the second user learned from the interaction between the first user and the second user;
- update a machine learning pairing algorithm based at least on the indication weighted by the machine learning feature, the machine learning pairing algorithm being configured to generate pairs of users based at least on user profiles and user ratings in response to interactions with other users of the interaction-based recommendation tool;
- and
- determine, by executing the updated machine learning pairing algorithm, to pair the first user with a third user based at least on a second compatibility score between the first user and the third user, that is determined from a first profile of the first user, a second profile of the third user, and the interaction history.
11 more elements
A method comprising:
- determining, by a processor, that a first user takes a first action with respect to an interaction-based recommendation tool;
- extracting, by the processor, a first set of features from a preference history of the first user, the first set of features indicating preferences of the first user with respect to other users;
- extracting, by the processor, a second set of features from a preference history of a second user, the second set of features indicating preferences of the second user with respect to other users;
- determining, by the processor, to pair the first user and the second user based at least on the first action, the first set of features, and the second set of features;
- pairing, by the processor, the first user and the second user for an interaction, the interaction lasting for a first time period, the interaction is concurrent for the first user and the second user;
- determining an interaction history related to the interaction;
- receiving at a second time, by an interface, an indication of a preference of the first user for the second user, the second time being after the first time period, the indication is one of user ratings;
- extracting a machine learning feature from the indication, the machine learning feature indicating a compatibility score between the first user and the second user learned from the interaction between the first user and the second user;
- updating, by the processor, a machine learning pairing algorithm based at least on the indication weighted by the machine learning feature, the machine learning pairing algorithm being configured to generate pairs of users based at least on user profiles and user ratings in response to interactions with other users of the interaction-based recommendation tool;
- and
- determining, by executing the updated machine learning pairing algorithm, to pair the first user with a third user based at least on a second compatibility score between the first user and the third user, that is determined from a first profile of the first user, a second profile of the third user, and the interaction history.
8 more elements
At least one non-transitory computer-readable medium comprising a plurality of instructions that, when executed by at least one processor, are configured to:
- determine that a first user takes a first action with respect to an interaction-based recommendation tool;
- extract a first set of features from a preference history of the first user, the first set of features indicating preferences of the first user with respect to other users;
- extract a second set of features from a preference history of a second user, the second set of features indicating preferences of the second user with respect to other users;
- determine to pair the first user and the second user based at least on the first action, the first set of features, and the second set of features;
- pair the first user and the second user for an interaction, the interaction lasting for a first time period, the interaction is concurrent for the first user and the second user;
- determine an interaction history related to the interaction;
- receive, at a second time, an indication of preference of the first user for the second user, the second time being after the first time period, the indication is one of the user ratings;
- extract a machine learning feature from the indication, the machine learning feature indicating a compatibility score between the first user and the second user learned from the interaction between the first user and the second user;
- update a machine learning pairing algorithm based at least on the indication weighted by the machine learning feature, the machine learning pairing algorithm being configured to generate pairs of users based at least on user profiles and user ratings in response to interactions with other users of the interaction-based recommendation tool;
- and
- determine, by executing the updated machine learning pairing algorithm, to pair the first user with a third user based at least on a second compatibility score between the first user and the third user, that is determined from a first profile of the first user, a second profile of the third user, and the interaction history.
8 more elements
Dependencies are inferred from the claim text.