Method
getRecommendations POST
Copy POST

Returns a list of recommendations for a given user under the specified project deployment. Note that the inputs to this method, wherever applicable, will be the column names in your dataset mapped to the column mappings in our system (e.g. column 'time' mapped to mapping 'TIMESTAMP' in our system).

Arguments:

REQUIRED KEY TYPE DESCRIPTION
Yes deploymentToken str The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
Yes deploymentId str The unique identifier to a deployment created under the project.
Yes queryData dict This will be a dictionary where 'Key' will be the column name (e.g. a column with name 'user_name' in your dataset) mapped to the column mapping USER_ID that uniquely identifies the user against which recommendations are made and 'Value' will be the unique value of the same item. For example, if you have the column name 'user_name' mapped to the column mapping 'USER_ID', then the query must have the exact same column name (user_name) as key and the name of the user (John Doe) as value.
No numItems int The number of items to recommend on one page. By default, it is set to 50 items per page.
No page int The page number to be displayed. For example, let's say that the num_items is set to 10 with the total recommendations list size of 50 recommended items, then an input value of 2 in the 'page' variable will display a list of items that rank from 11th to 20th.
No excludeItemIds list [DEPRECATED]
No scoreField str The relative item scores are returned in a separate field named with the same name as the key (score_field) for this argument.
No scalingFactors list It allows you to bias the model towards certain items. The input to this argument is a list of dictionaries where the format of each dictionary is as follows: {"column": "col0", "values": ["value0", "value1"], "factor": 1.1}. The key, "column" takes the name of the column, "col0"; the key, "values" takes the list of items, "["value0", "value1"]" in reference to which the model recommendations need to be biased; and the key, "factor" takes the factor by which the item scores are adjusted. Let's take an example where the input to scaling_factors is [{"column": "VehicleType", "values": ["SUV", "Sedan"], "factor": 1.4}]. After we apply the model to get item probabilities, for every SUV and Sedan in the list, we will multiply the respective probability by 1.1 before sorting. This is particularly useful if there's a type of item that might be less popular but you want to promote it or there's an item that always comes up and you want to demote it.
No restrictItems list It allows you to restrict the recommendations to certain items. The input to this argument is a list of dictionaries where the format of each dictionary is as follows: {"column": "col0", "values": ["value0", "value1", "value3", ...]}. The key, "column" takes the name of the column, "col0"; the key, "values" takes the list of items, "["value0", "value1", "value3", ...]" to which to restrict the recommendations to. Let's take an example where the input to restrict_items is [{"column": "VehicleType", "values": ["SUV", "Sedan"]}]. This input will restrict the recommendations to SUVs and Sedans. This type of restriction is particularly useful if there's a list of items that you know is of use in some particular scenario and you want to restrict the recommendations only to that list.
No excludeItems list It allows you to exclude certain items from the list of recommendations. The input to this argument is a list of dictionaries where the format of each dictionary is as follows: {"column": "col0", "values": ["value0", "value1", ...]}. The key, "column" takes the name of the column, "col0"; the key, "values" takes the list of items, "["value0", "value1"]" to exclude from the recommendations. Let's take an example where the input to exclude_items is [{"column": "VehicleType", "values": ["SUV", "Sedan"]}]. The resulting recommendation list will exclude all SUVs and Sedans. This is
No exploreFraction float Explore fraction.
No diversityAttributeName str item attribute column name which is used to ensure diversity of prediction results.
No diversityMaxResultsPerValue int maximum number of results per value of diversity_attribute_name.
Note: The arguments for the API methods follow camelCase but for Python SDK underscore_case is followed.

Response:

KEY TYPE DESCRIPTION
success Boolean true if the call succeeded, false if there was an error
RecommendationPrediction

Exceptions:

TYPE WHEN
DataNotFoundError

deploymentId is not found.
Language:

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