Patent ID: 11861538
Assignee: AMAZON TECHNOLOGIES, INC.
Field: IT methods for management (Electrical engineering)
Classification: CPC G | IPC G

Claim 5:
6. A computer-implemented method, comprising:
performing, by a machine learning system implemented using one or more computing devices comprising respective processors and memory:
determining respective values of a plurality of attributes of a first objective associated with a first offering set comprising one or more network-accessible offerings, wherein the respective values indicate at least (a) one or more types of desired user actions and (b) one or more offering set-specific metrics associated with the one or more types of desired user actions;
obtaining, from one or more strategy providers, a plurality of user selection strategies for the first objective, wherein a user selection strategy of the plurality of user selection strategies assigns respective selection probabilities of receiving at least some content associated with the first offering set to individual users of a user population;
provisioning one or more compute resources to perform a machine learning process with respect to a first sub-sample of the user population;
executing the machine learning process using the one or more compute resources, wherein the execution of the machine learning process includes a plurality of optimization iterations using at least a first subset of user selection strategies of the plurality of user selection strategies, wherein an individual optimization iteration comprises at least:
computing respective aggregated selection probabilities for individual users of the first sub-sample based at least in part on (a) per-strategy selection probabilities for the individual users, obtained from individual ones of the first subset of user selection strategies and (b) respective weights assigned to individual ones of the first subset of user selection strategies;
sending, to one or more content presentation computers over one or more networks, based at least in art on the aggregated selection probabilities, one or more content presentation requests of at least some content associated with the first offering set, wherein the content presentation computers are configured to present content according to the one or more content presentation requests to at least some users of the first sub-sample;
receiving, from the one or more contention presentation computers and over the one or more networks, one or more feedback metrics associated with presentation of the content over a selected time interval, including (a) at least one offering set-specific metric of the one or more offering set-specific metrics and (b) at least one resource usage metric associated with the presentation of the content; and
updating at least some weights of the respective weights, based at least in part on (a) analysis of the one or more feedback metrics and (b) an exploration-exploitation tradeoff parameter that controls the machine learning process;

terminating the machine learning process when an optimization termination criterion of the machine learning process is met, wherein the termination criterion is based at least in part on a specified amount of compute resources consumed by the machine learning process;
causing, in accordance with a set of weights updated by the machine learning process, at least some content associated with the first offering set to be presented to at least some users of the user population which were not in the first sub-sample; and
adding one or more records of the first objective to a repository of records, wherein the one or more records are used to select user selection strategies for later offering sets and reduce time and resources used to optimize presentation of the later offering sets.