Service object for interacting with AWS PersonalizeRuntime service.
public struct PersonalizeRuntime: AWSService
Initialize the PersonalizeRuntime client
public init( client: AWSClient, region: SotoCore.Region? = nil, partition: AWSPartition = .aws, endpoint: String? = nil, timeout: TimeAmount? = nil, byteBufferAllocator: ByteBufferAllocator = ByteBufferAllocator(), options: AWSServiceConfig.Options =  )
- client: AWSClient used to process requests
- region: Region of server you want to communicate with. This will override the partition parameter.
- partition: AWS partition where service resides, standard (.aws), china (.awscn), government (.awsusgov).
- endpoint: Custom endpoint URL to use instead of standard AWS servers
- timeout: Timeout value for HTTP requests
Initializer required by
AWSService.with(middlewares:timeout:byteBufferAllocator:options). You are not able to use this initializer directly as there are no public initializers for
AWSServiceConfig.Patch. Please use
public init(from: PersonalizeRuntime, patch: AWSServiceConfig.Patch)
Client used for communication with AWS
public let client: AWSClient
public let config: AWSServiceConfig
public func getPersonalizedRanking(_ input: GetPersonalizedRankingRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<GetPersonalizedRankingResponse>
Re-ranks a list of recommended items for the given user. The first item in the list is deemed the most likely item to be of interest to the user.
The solution backing the campaign must have been created using a recipe of type PERSONALIZED_RANKING.
public func getRecommendations(_ input: GetRecommendationsRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<GetRecommendationsResponse>
Returns a list of recommended items. For campaigns, the campaign's Amazon Resource Name (ARN) is required and the required user and item input depends on the recipe type used to create the solution backing the campaign as follows:
Campaigns that are backed by a solution created using a recipe of type PERSONALIZED_RANKING use the API.
For recommenders, the recommender's ARN is required and the required item and user input depends on the use case (domain-based recipe) backing the recommender. For information on use case requirements see Choosing recommender use cases.