EventDetail(mapping=None, *, ignore_unknown_fields=False, **kwargs)User event details shared by all recommendation types.
Attributes |
|
|---|---|
| Name | Description |
uri |
str
Optional. Complete url (window.location.href) of the user's current page. When using the JavaScript pixel, this value is filled in automatically. Maximum length 5KB. |
referrer_uri |
str
Optional. The referrer url of the current page. When using the JavaScript pixel, this value is filled in automatically. |
page_view_id |
str
Optional. A unique id of a web page view. This should be kept the same for all user events triggered from the same pageview. For example, an item detail page view could trigger multiple events as the user is browsing the page. The pageViewId property should be kept the same for all
these events so that they can be grouped together properly.
This pageViewId will be automatically generated if using
the JavaScript pixel.
|
experiment_ids |
MutableSequence[str]
Optional. A list of identifiers for the independent experiment groups this user event belongs to. This is used to distinguish between user events associated with different experiment setups (e.g. using Recommendation Engine system, using different recommendation models). |
recommendation_token |
str
Optional. Recommendation token included in the recommendation prediction response. This field enables accurate attribution of recommendation model performance. This token enables us to accurately attribute page view or purchase back to the event and the particular predict response containing this clicked/purchased item. If user clicks on product K in the recommendation results, pass the PredictResponse.recommendationToken property as a url
parameter to product K's page. When recording events on
product K's page, log the
PredictResponse.recommendation_token to this field.
Optional, but highly encouraged for user events that are the
result of a recommendation prediction query.
|
event_attributes |
google.cloud.recommendationengine_v1beta1.types.FeatureMap
Optional. Extra user event features to include in the recommendation model. For product recommendation, an example of extra user information is traffic_channel, i.e. how user arrives at the site. Users can arrive at the site by coming to the site directly, or coming through Google search, and etc. |