rec_endpoint: Azure product recommendations endpoint class

Description Format Methods Initialization Training See Also Examples

Description

Class representing the client endpoint to the product recommendations service.

Format

An R6 object of class rec_endpoint.

Methods

Initialization

The following arguments are used to initialize a new client endpoint:

Note that the name of the client endpoint for a product recommendations service is not the name that was supplied when deploying the service. Instead, it is a randomly generated unique string that starts with the service name. For example, if you deployed a service called "myrec", the name of the endpoint is "myrecusacvjwpk4raost".

Training

To train a new model, supply the following arguments to the train_model method:

For detailed information on these arguments see the API reference.

See Also

az_rec_service for the service itself, rec_model for an individual recommmendations model

API reference and SAR model description at the Product Recommendations API repo on GitHub

Examples

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## Not run: 

# creating a recommendations service endpoint from an Azure resource
svc <- resgroup$get_rec_service("myrec")
rec_endp <- svc$get_rec_endpoint()

# creating the endpoint from scratch -- must supply admin, recommender and storage keys
rec_endp <- rec_endpoint$new("myrecusacvjwpk4raost",
    admin_key="key1", rec_key="key2", storage_key="key3")

# upload the Microsoft store data
data(ms_usage)
rec_endp$upload_data(ms_usage)

# train a recommender
rec_model <- rec_endp$train_model("model1", usage="ms_usage.csv", support_threshold=10,
    similarity="Jaccard", user_affinity=TRUE, user_to_items=TRUE,
    backfill=TRUE, include_seed_items=FALSE)

# list of trained models
rec_endp$sync_model_list()

# delete the trained model (will ask for confirmation)
rec_endp$delete_model("model1")


## End(Not run)

SAR documentation built on Oct. 23, 2020, 7:55 p.m.