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    REST Resource: projects.locations.catalogs.models

    Resource: Model

    Metadata that describes the training and serving parameters of a Model . A Model can be associated with a ServingConfig and then queried through the Predict API.

    JSON representation 
     { 
 "name" 
 : 
 string 
 , 
 "displayName" 
 : 
 string 
 , 
 "trainingState" 
 : 
 enum (  TrainingState 
 
) 
 , 
 "servingState" 
 : 
 enum (  ServingState 
 
) 
 , 
 "createTime" 
 : 
 string 
 , 
 "updateTime" 
 : 
 string 
 , 
 "type" 
 : 
 string 
 , 
 "optimizationObjective" 
 : 
 string 
 , 
 "periodicTuningState" 
 : 
 enum (  PeriodicTuningState 
 
) 
 , 
 "lastTuneTime" 
 : 
 string 
 , 
 "tuningOperation" 
 : 
 string 
 , 
 "dataState" 
 : 
 enum (  DataState 
 
) 
 , 
 "filteringOption" 
 : 
 enum (  RecommendationsFilteringOption 
 
) 
 , 
 "servingConfigLists" 
 : 
 [ 
 { 
 object (  ServingConfigList 
 
) 
 } 
 ] 
 , 
 "modelFeaturesConfig" 
 : 
 { 
 object (  ModelFeaturesConfig 
 
) 
 } 
 , 
 // Union field training_config 
can be only one of the following: 
 "pageOptimizationConfig" 
 : 
 { 
 object (  PageOptimizationConfig 
 
) 
 } 
 // End of list of possible types for union field training_config 
. 
 }
    Fields
    name

    string

    Required. The fully qualified resource name of the model.

    Format: projects/{projectNumber}/locations/{locationId}/catalogs/{catalogId}/models/{modelId} catalogId has char limit of 50. recommendation_model_id has char limit of 40.

    displayName

    string

    Required. The display name of the model.

    Should be human readable, used to display Recommendation Models in the Retail Cloud Console Dashboard. UTF-8 encoded string with limit of 1024 characters.

    trainingState

    enum ( TrainingState )

    Optional. The training state that the model is in (e.g. TRAINING or PAUSED ).

    Since part of the cost of running the service is frequency of training - this can be used to determine when to train model in order to control cost. If not specified: the default value for models.create method is TRAINING . The default value for models.patch method is to keep the state the same as before.

    servingState

    enum ( ServingState )

    Output only. The serving state of the model: ACTIVE , NOT_ACTIVE .

    createTime

    string ( Timestamp format)

    Output only. Timestamp the Recommendation Model was created at.

    Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples: "2014-10-02T15:01:23Z" , "2014-10-02T15:01:23.045123456Z" or "2014-10-02T15:01:23+05:30" .

    updateTime

    string ( Timestamp format)

    Output only. Timestamp the Recommendation Model was last updated. E.g. if a Recommendation Model was paused - this would be the time the pause was initiated.

    Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples: "2014-10-02T15:01:23Z" , "2014-10-02T15:01:23.045123456Z" or "2014-10-02T15:01:23+05:30" .

    type

    string

    Required. The type of model e.g. home-page .

    Currently supported values: recommended-for-you , others-you-may-like , frequently-bought-together , page-optimization , similar-items , buy-it-again , on-sale-items , and recently-viewed (readonly value).

    This field together with optimizationObjective describe model metadata to use to control model training and serving. See https://cloud.google.com/retail/docs/models for more details on what the model metadata control and which combination of parameters are valid. For invalid combinations of parameters (e.g. type = frequently-bought-together and optimizationObjective = ctr ), you receive an error 400 if you try to create/update a recommendation with this set of knobs.

    optimizationObjective

    string

    Optional. The optimization objective e.g. cvr .

    Currently supported values: ctr , cvr , revenue-per-order .

    If not specified, we choose default based on model type. Default depends on type of recommendation:

    recommended-for-you => ctr

    others-you-may-like => ctr

    frequently-bought-together => revenue_per_order

    This field together with optimizationObjective describe model metadata to use to control model training and serving. See https://cloud.google.com/retail/docs/models for more details on what the model metadata control and which combination of parameters are valid. For invalid combinations of parameters (e.g. type = frequently-bought-together and optimizationObjective = ctr ), you receive an error 400 if you try to create/update a recommendation with this set of knobs.

    periodicTuningState

    enum ( PeriodicTuningState )

    Optional. The state of periodic tuning.

    The period we use is 3 months - to do a one-off tune earlier use the models.tune method. Default value is PERIODIC_TUNING_ENABLED .

    lastTuneTime

    string ( Timestamp format)

    Output only. The timestamp when the latest successful tune finished.

    Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples: "2014-10-02T15:01:23Z" , "2014-10-02T15:01:23.045123456Z" or "2014-10-02T15:01:23+05:30" .

    tuningOperation

    string

    Output only. The tune operation associated with the model.

    Can be used to determine if there is an ongoing tune for this recommendation. Empty field implies no tune is goig on.

    dataState

    enum ( DataState )

    Output only. The state of data requirements for this model: DATA_OK and DATA_ERROR .

    Recommendation model cannot be trained if the data is in DATA_ERROR state. Recommendation model can have DATA_ERROR state even if serving state is ACTIVE : models were trained successfully before, but cannot be refreshed because model no longer has sufficient data for training.

    filteringOption

    enum ( RecommendationsFilteringOption )

    Optional. If RECOMMENDATIONS_FILTERING_ENABLED , recommendation filtering by attributes is enabled for the model.

    servingConfigLists[]

    object ( ServingConfigList )

    Output only. The list of valid serving configs associated with the PageOptimizationConfig.

    modelFeaturesConfig

    object ( ModelFeaturesConfig )

    Optional. Additional model features config.

    Union field training_config . Training configuration specific to a Model.type - currently, only for page optimization. training_config can be only one of the following:
    pageOptimizationConfig

    object ( PageOptimizationConfig )

    Optional. The page optimization config.

    PageOptimizationConfig

    The PageOptimizationConfig for model training.

    This determines how many panels to optimize for, and which serving configs to consider for each panel. The purpose of this model is to optimize which ServingConfig to show on which panels in way that optimizes the visitors shopping journey.

    JSON representation 
     { 
 "pageOptimizationEventType" 
 : 
 string 
 , 
 "panels" 
 : 
 [ 
 { 
 object (  Panel 
 
) 
 } 
 ] 
 , 
 "restriction" 
 : 
 enum (  Restriction 
 
) 
 }
    Fields
    pageOptimizationEventType

    string

    Required. The type of UserEvent this page optimization is shown for.

    Each page has an associated event type - this will be the corresponding event type for the page that the page optimization model is used on.

    Supported types:

    • add-to-cart : Products being added to cart.
    • detail-page-view : Products detail page viewed.
    • home-page-view : Homepage viewed
    • category-page-view : Homepage viewed
    • shopping-cart-page-view : User viewing a shopping cart.

    home-page-view only allows models with type recommended-for-you . All other pageOptimizationEventType allow all Model.types .

    panels[]

    object ( Panel )

    Required. A list of panel configurations.

    Limit = 5.

    restriction

    enum ( Restriction )

    Optional. How to restrict results across panels e.g. can the same ServingConfig be shown on multiple panels at once.

    If unspecified, default to UNIQUE_MODEL_RESTRICTION .

    Panel

    An individual panel with a list of ServingConfigs to consider for it.

    JSON representation 
     { 
 "displayName" 
 : 
 string 
 , 
 "candidates" 
 : 
 [ 
 { 
 object (  Candidate 
 
) 
 } 
 ] 
 , 
 "defaultCandidate" 
 : 
 { 
 object (  Candidate 
 
) 
 } 
 }
    Fields
    displayName

    string

    Optional. The name to display for the panel.
    candidates[]

    object ( Candidate )

    Required. The candidates to consider on the panel.
    defaultCandidate

    object ( Candidate )

    Required. The default candidate. If the model fails at serving time, we fall back to the default.

    Candidate

    A candidate to consider for a given panel. Currently only ServingConfig are valid candidates.

    JSON representation 
     { 
 // Union field candidate 
can be only one of the following: 
 "servingConfigId" 
 : 
 string 
 // End of list of possible types for union field candidate 
. 
 }
    Fields

    Union field candidate .

    candidate can be only one of the following:

    servingConfigId

    string

    This has to be a valid ServingConfig identifier. For example, for a ServingConfig with full name: projects/*/locations/global/catalogs/default_catalog/servingConfigs/my_candidate_config , this would be my_candidate_config .

    Restriction

    Restrictions of expected returned results.

    Enums
    RESTRICTION_UNSPECIFIED
    Unspecified value for restriction.
    NO_RESTRICTION

    Allow any ServingConfig to be show on any number of panels.

    Example:

    Panel1 candidates : pdp_ctr, pdp_cvr, home_page_ctr_no_diversity

    Panel2 candidates : home_page_ctr_no_diversity, home_page_ctr_diversity, pdp_cvr_no_diversity

    Restriction = NO_RESTRICTION

    Valid combinations :

    • (pdp_ctr, home_page_ctr_no_diversity)
    • (pdp_ctr, home_page_ctr_diversity)
    • (pdp_ctr, pdp_cvr_no_diversity)
    • (pdp_cvr, home_page_ctr_no_diversity)
    • (pdp_cvr, home_page_ctr_diversity)
    • (pdp_cvr, pdp_cvr_no_diversity)
    • (home_page_ctr_no_diversity, home_page_ctr_no_diversity)
    • (home_page_ctr_no_diversity, home_page_ctr_diversity)
    • (home_page_ctr_no_diversity, pdp_cvr_no_diversity)

    Invalid combinations : []

    UNIQUE_SERVING_CONFIG_RESTRICTION

    Do not allow the same ServingConfig.name to be shown on multiple panels.

    Example:

    Panel1 candidates : pdp_ctr, pdp_cvr, home_page_ctr_no_diversity

    Panel2 candidates : home_page_ctr_no_diversity, home_page_ctr_diversity_low, pdp_cvr_no_diversity

    Restriction = UNIQUE_SERVING_CONFIG_RESTRICTION

    Valid combinations :

    • (pdp_ctr, home_page_ctr_no_diversity)
    • (pdp_ctr, home_page_ctr_diversity_low)
    • (pdp_ctr, pdp_cvr_no_diversity)
    • (pdp_ctr, pdp_cvr_no_diversity)
    • (pdp_cvr, home_page_ctr_no_diversity)
    • (pdp_cvr, home_page_ctr_diversity_low)
    • (pdp_cvr, pdp_cvr_no_diversity)
    • (home_page_ctr_no_diversity, home_page_ctr_diversity_low)
    • (home_page_ctr_no_diversity, pdp_cvr_no_diversity)

    Invalid combinations :

    • (home_page_ctr_no_diversity, home_page_ctr_no_diversity)
    UNIQUE_MODEL_RESTRICTION

    Do not allow multiple ServingConfigs with same Model.name to be show on on different panels.

    Example:

    Panel1 candidates : pdp_ctr, pdp_cvr, home_page_ctr_no_diversity

    Panel2 candidates : home_page_ctr_no_diversity, home_page_ctr_diversity_low, pdp_cvr_no_diversity

    Restriction = UNIQUE_MODEL_RESTRICTION

    Valid combinations :

    • (pdp_ctr, home_page_ctr_no_diversity)
    • (pdp_ctr, home_page_ctr_diversity)
    • (pdp_ctr, pdp_cvr_no_diversity)
    • (pdp_ctr, pdp_cvr_no_diversity)
    • (pdp_cvr, home_page_ctr_no_diversity)
    • (pdp_cvr, home_page_ctr_diversity_low)
    • (home_page_ctr_no_diversity, pdp_cvr_no_diversity)

    Invalid combinations :

    • (home_page_ctr_no_diversity, home_page_ctr_no_diversity)
    • (pdp_cvr, pdp_cvr_no_diversity)
    UNIQUE_MODEL_TYPE_RESTRICTION

    Do not allow multiple ServingConfigs with same Model.type to be shown on different panels.

    Example:

    Panel1 candidates : pdp_ctr, pdp_cvr, home_page_ctr_no_diversity

    Panel2 candidates : home_page_ctr_no_diversity, home_page_ctr_diversity_low, pdp_cvr_no_diversity

    Restriction = UNIQUE_MODEL_RESTRICTION

    Valid combinations :

    • (pdp_ctr, home_page_ctr_no_diversity)
    • (pdp_ctr, home_page_ctr_diversity)
    • (pdp_cvr, home_page_ctr_no_diversity)
    • (pdp_cvr, home_page_ctr_diversity_low)
    • (home_page_ctr_no_diversity, pdp_cvr_no_diversity)

    Invalid combinations :

    • (pdp_ctr, pdp_cvr_no_diversity)
    • (pdp_ctr, pdp_cvr_no_diversity)
    • (pdp_cvr, pdp_cvr_no_diversity)
    • (home_page_ctr_no_diversity, home_page_ctr_no_diversity)
    • (home_page_ctr_no_diversity, home_page_ctr_diversity)

    TrainingState

    The training state of the model.

    Enums
    TRAINING_STATE_UNSPECIFIED Unspecified training state.
    PAUSED The model training is paused.
    TRAINING The model is training.

    ServingState

    The serving state of the model.

    Enums
    SERVING_STATE_UNSPECIFIED Unspecified serving state.
    INACTIVE The model is not serving.
    ACTIVE The model is serving and can be queried.
    TUNED The model is trained on tuned hyperparameters and can be queried.

    PeriodicTuningState

    Describes whether periodic tuning is enabled for this model or not. Periodic tuning is scheduled at most every three months. You can start a tuning process manually by using the models.tune method, which starts a tuning process immediately and resets the quarterly schedule. Enabling or disabling periodic tuning does not affect any current tuning processes.

    Enums
    PERIODIC_TUNING_STATE_UNSPECIFIED Unspecified default value, should never be explicitly set.
    PERIODIC_TUNING_DISABLED The model has periodic tuning disabled. Tuning can be reenabled by calling the EnableModelPeriodicTuning method or by calling the models.tune method.
    ALL_TUNING_DISABLED The model cannot be tuned with periodic tuning OR the models.tune method. Hide the options in customer UI and reject any requests through the backend self serve API.
    PERIODIC_TUNING_ENABLED The model has periodic tuning enabled. Tuning can be disabled by calling the DisableModelPeriodicTuning method.

    DataState

    Describes whether this model have sufficient training data to be continuously trained.

    Enums
    DATA_STATE_UNSPECIFIED Unspecified default value, should never be explicitly set.
    DATA_OK The model has sufficient training data.
    DATA_ERROR The model does not have sufficient training data. Error messages can be queried via Stackdriver.

    ServingConfigList

    Represents an ordered combination of valid serving configs, which can be used for PAGE_OPTIMIZATION recommendations.

    JSON representation 
     { 
 "servingConfigIds" 
 : 
 [ 
 string 
 ] 
 }
    Fields
    servingConfigIds[]

    string

    Optional. A set of valid serving configs that may be used for PAGE_OPTIMIZATION .

    ModelFeaturesConfig

    Additional model features config.

    JSON representation 
     { 
 // Union field type_dedicated_config 
can be only one of the following: 
 "frequentlyBoughtTogetherConfig" 
 : 
 { 
 object (  FrequentlyBoughtTogetherFeaturesConfig 
 
) 
 } 
 // End of list of possible types for union field type_dedicated_config 
. 
 }
    Fields

    Union field type_dedicated_config .

    type_dedicated_config can be only one of the following:

    frequentlyBoughtTogetherConfig

    object ( FrequentlyBoughtTogetherFeaturesConfig )

    Additional configs for frequently-bought-together models.

    FrequentlyBoughtTogetherFeaturesConfig

    Additional configs for the frequently-bought-together model type.

    JSON representation 
     { 
 "contextProductsType" 
 : 
 enum (  ContextProductsType 
 
) 
 }
    Fields
    contextProductsType

    enum ( ContextProductsType )

    Optional. Specifies the context of the model when it is used in predict requests. Can only be set for the frequently-bought-together type. If it isn't specified, it defaults to MULTIPLE_CONTEXT_PRODUCTS .

    ContextProductsType

    Use single or multiple context products for recommendations.

    Enums
    CONTEXT_PRODUCTS_TYPE_UNSPECIFIED Unspecified default value, should never be explicitly set. Defaults to MULTIPLE_CONTEXT_PRODUCTS .
    SINGLE_CONTEXT_PRODUCT Use only a single product as context for the recommendation. Typically used on pages like add-to-cart or product details.
    MULTIPLE_CONTEXT_PRODUCTS Use one or multiple products as context for the recommendation. Typically used on shopping cart pages.

    Methods

    create

    		Creates a new model.

    delete

    		Deletes an existing model.

    get

    		Gets a model.

    list

    		Lists all the models linked to this event store.

    patch

    		Update of model metadata.

    pause

    		Pauses the training of an existing model.

    resume

    		Resumes the training of an existing model.

    tune

    		Tunes an existing model.
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