Google Cloud Recommendation Engine V1beta1 Client - Class PredictRequest (0.7.7)

Reference documentation and code samples for the Google Cloud Recommendation Engine V1beta1 Client class PredictRequest.

Request message for Predict method.

Generated from protobuf message google.cloud.recommendationengine.v1beta1.PredictRequest

Constructor.

Required. Full resource name of the format: {name=projects/*/locations/global/catalogs/default_catalog/eventStores/default_event_store/placements/*} The id of the recommendation engine placement. This id is used to identify the set of models that will be used to make the prediction.

We currently support three placements with the following IDs by default:

• shopping_cart : Predicts items frequently bought together with one or more catalog items in the same shopping session. Commonly displayed after add-to-cart events, on product detail pages, or on the shopping cart page.
• home_page : Predicts the next product that a user will most likely engage with or purchase based on the shopping or viewing history of the specified userId or visitorId . For example - Recommendations for you.
• product_detail : Predicts the next product that a user will most likely engage with or purchase. The prediction is based on the shopping or viewing history of the specified userId or visitorId and its relevance to a specified CatalogItem . Typically used on product detail pages. For example - More items like this.
• recently_viewed_default : Returns up to 75 items recently viewed by the specified userId or visitorId , most recent ones first. Returns nothing if neither of them has viewed any items yet. For example - Recently viewed. The full list of available placements can be seen at https://console.cloud.google.com/recommendation/datafeeds/default_catalog/dashboard

Required. Full resource name of the format: {name=projects/*/locations/global/catalogs/default_catalog/eventStores/default_event_store/placements/*} The id of the recommendation engine placement. This id is used to identify the set of models that will be used to make the prediction.

We currently support three placements with the following IDs by default:

• shopping_cart : Predicts items frequently bought together with one or more catalog items in the same shopping session. Commonly displayed after add-to-cart events, on product detail pages, or on the shopping cart page.
• home_page : Predicts the next product that a user will most likely engage with or purchase based on the shopping or viewing history of the specified userId or visitorId . For example - Recommendations for you.
• product_detail : Predicts the next product that a user will most likely engage with or purchase. The prediction is based on the shopping or viewing history of the specified userId or visitorId and its relevance to a specified CatalogItem . Typically used on product detail pages. For example - More items like this.
• recently_viewed_default : Returns up to 75 items recently viewed by the specified userId or visitorId , most recent ones first. Returns nothing if neither of them has viewed any items yet. For example - Recently viewed. The full list of available placements can be seen at https://console.cloud.google.com/recommendation/datafeeds/default_catalog/dashboard

Required. Context about the user, what they are looking at and what action they took to trigger the predict request. Note that this user event detail won't be ingested to userEvent logs. Thus, a separate userEvent write request is required for event logging.

Required. Context about the user, what they are looking at and what action they took to trigger the predict request. Note that this user event detail won't be ingested to userEvent logs. Thus, a separate userEvent write request is required for event logging.

Optional. Maximum number of results to return per page. Set this property to the number of prediction results required. If zero, the service will choose a reasonable default.

Optional. Maximum number of results to return per page. Set this property to the number of prediction results required. If zero, the service will choose a reasonable default.

Optional. The previous PredictResponse.next_page_token.

Optional. The previous PredictResponse.next_page_token.

Optional. Filter for restricting prediction results. Accepts values for tags and the filterOutOfStockItems flag.

• Tag expressions. Restricts predictions to items that match all of the specified tags. Boolean operators OR and NOT are supported if the expression is enclosed in parentheses, and must be separated from the tag values by a space. -"tagA" is also supported and is equivalent to NOT "tagA" . Tag values must be double quoted UTF-8 encoded strings with a size limit of 1 KiB.
  • filterOutOfStockItems. Restricts predictions to items that do not have a stockState value of OUT_OF_STOCK. Examples:
  • tag=("Red" OR "Blue") tag="New-Arrival" tag=(NOT "promotional")
  • filterOutOfStockItems tag=(-"promotional")
  • filterOutOfStockItems

Optional. Filter for restricting prediction results. Accepts values for tags and the filterOutOfStockItems flag.

• Tag expressions. Restricts predictions to items that match all of the specified tags. Boolean operators OR and NOT are supported if the expression is enclosed in parentheses, and must be separated from the tag values by a space. -"tagA" is also supported and is equivalent to NOT "tagA" . Tag values must be double quoted UTF-8 encoded strings with a size limit of 1 KiB.
  • filterOutOfStockItems. Restricts predictions to items that do not have a stockState value of OUT_OF_STOCK. Examples:
  • tag=("Red" OR "Blue") tag="New-Arrival" tag=(NOT "promotional")
  • filterOutOfStockItems tag=(-"promotional")
  • filterOutOfStockItems

Optional. Use dryRun mode for this prediction query. If set to true, a dummy model will be used that returns arbitrary catalog items.

Note that the dryRun mode should only be used for testing the API, or if the model is not ready.

Optional. Use dryRun mode for this prediction query. If set to true, a dummy model will be used that returns arbitrary catalog items.

Note that the dryRun mode should only be used for testing the API, or if the model is not ready.

Optional. Additional domain specific parameters for the predictions.

Allowed values:

• returnCatalogItem : Boolean. If set to true, the associated catalogItem object will be returned in the PredictResponse.PredictionResult.itemMetadata object in the method response.
• returnItemScore : Boolean. If set to true, the prediction 'score' corresponding to each returned item will be set in the metadata field in the prediction response. The given 'score' indicates the probability of an item being clicked/purchased given the user's context and history.

Optional. Additional domain specific parameters for the predictions.

Allowed values:

• returnCatalogItem : Boolean. If set to true, the associated catalogItem object will be returned in the PredictResponse.PredictionResult.itemMetadata object in the method response.
• returnItemScore : Boolean. If set to true, the prediction 'score' corresponding to each returned item will be set in the metadata field in the prediction response. The given 'score' indicates the probability of an item being clicked/purchased given the user's context and history.

Optional. The labels for the predict request.

• Label keys can contain lowercase letters, digits and hyphens, must start with a letter, and must end with a letter or digit.
  • Non-zero label values can contain lowercase letters, digits and hyphens, must start with a letter, and must end with a letter or digit.
  • No more than 64 labels can be associated with a given request. See https://goo.gl/xmQnxf for more information on and examples of labels.

Optional. The labels for the predict request.

• Label keys can contain lowercase letters, digits and hyphens, must start with a letter, and must end with a letter or digit.
  • Non-zero label values can contain lowercase letters, digits and hyphens, must start with a letter, and must end with a letter or digit.
  • No more than 64 labels can be associated with a given request. See https://goo.gl/xmQnxf for more information on and examples of labels.