Get Model Result
Constructors
Types
Properties
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.
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 optimization_objective 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 optimization_objective = ctr
), you receive an error 400 if you try to create/update a recommendation with this set of knobs.
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 CreateModel
method is TRAINING
. The default value for UpdateModel
method is to keep the state the same as before.
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 optimization_objective 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 optimization_objective = ctr
), you receive an error 400 if you try to create/update a recommendation with this set of knobs.