Struct GoogleCloudRecommendationengineV1beta1PredictRequest 

pub struct GoogleCloudRecommendationengineV1beta1PredictRequest {
    pub dry_run: Option<bool>,
    pub filter: Option<String>,
    pub labels: Option<HashMap<String, String>>,
    pub page_size: Option<i32>,
    pub page_token: Option<String>,
    pub params: Option<HashMap<String, Value>>,
    pub user_event: Option<GoogleCloudRecommendationengineV1beta1UserEvent>,
}

Request message for Predict method. 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 event, 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/catalogs/default_catalog/placements

Activities

This type is used in activities, which are methods you may call on this type or where this type is involved in. The list links the activity name, along with information about where it is used (one of request and response).

• locations catalogs event stores placements predict projects (request)

Fields

dry_run: Option<bool>

Optional. Use dryRun mode for this prediction query. If set to true, a fake 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.

filter: Option<String>

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 If your filter blocks all prediction results, nothing will be returned. If you want generic (unfiltered) popular items to be returned instead, set strictFiltering to false in PredictRequest.params.

labels: Option<HashMap<String, String>>

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.

page_size: Option<i32>

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.

page_token: Option<String>

Optional. The previous PredictResponse.next_page_token.

params: Option<HashMap<String, Value>>

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. * strictFiltering: Boolean. True by default. If set to false, the service will return generic (unfiltered) popular items instead of empty if your filter blocks all prediction results. * priceRerankLevel: String. Default empty. If set to be non-empty, then it needs to be one of {‘no-price-reranking’, ‘low-price-reranking’, ‘medium-price-reranking’, ‘high-price-reranking’}. This gives request level control and adjust prediction results based on product price. * diversityLevel: String. Default empty. If set to be non-empty, then it needs to be one of {‘no-diversity’, ‘low-diversity’, ‘medium-diversity’, ‘high-diversity’, ‘auto-diversity’}. This gives request level control and adjust prediction results based on product category.

user_event: Option<GoogleCloudRecommendationengineV1beta1UserEvent>

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. Don’t set UserInfo.visitor_id or UserInfo.user_id to the same fixed ID for different users. If you are trying to receive non-personalized recommendations (not recommended; this can negatively impact model performance), instead set UserInfo.visitor_id to a random unique ID and leave UserInfo.user_id unset.

Trait Implementations

impl Clone for GoogleCloudRecommendationengineV1beta1PredictRequest

fn clone(&self) -> GoogleCloudRecommendationengineV1beta1PredictRequest

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for GoogleCloudRecommendationengineV1beta1PredictRequest

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for GoogleCloudRecommendationengineV1beta1PredictRequest

fn default() -> GoogleCloudRecommendationengineV1beta1PredictRequest

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for GoogleCloudRecommendationengineV1beta1PredictRequest

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for GoogleCloudRecommendationengineV1beta1PredictRequest

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl RequestValue for GoogleCloudRecommendationengineV1beta1PredictRequest