API Reference

Swagger UI

Once you deploy the service, you will be able to navigate to the API Reference (Swagger definition) of your newly created service by going to https://<service_name>.azurewebsites.net/swagger . You will get the root url upon deploying your service.

The swagger UI is the authoritative definition of the API interface -- but we have duplicated a description of the API reference here so you can understand what the service will look like before you actually deploy it.

Quick Reference

Use the following links to quickly jump to the relevant documentation:

API Documentation Reference
Authentication
Train a New Model API
Get Model Information API
List Models API
Delete a Model API
Get Item-To-Item Recommendations API
Get Personalized Recommendations API
Set Default Model Id API
Get Default Model Information API
Get Item-To-Item Recommendations from the Default Model API
Get Personalized Recommendations from the Default Model API

Schemas Documentation Reference
Catalog File Schema
Usage Events File Schema
Model Object Schema
Model Training Parameters Schema
Model Training Statistics Schema
Parsing Report Schema
Parsing Error Schema
Model Evaluation Schema
Model Evaluation Metrics Schema
Precision Metric
Model Diversity Metrics
Diversity Percentile Bucket
Get Recommendations Usage Event
Recommendation Object Schema

Authentication

When calling the service, you will need to set authentication headers with the API key that is provided as part of the set-up process.

If you did not copy the keys at the time of setting up the preconfigured solution, you can find those by:

Logging into the Azure Portal
Navigating to the resource group that was created by the solution. The resource group is named after the deployment name you provided when you set up the solution.
Then navigate to App Service.
Click on Application Settings
The keys are under the App settings section, they are called AdminPrimaryKey, AdminSecondaryKey, RecommendPrimaryKey and RecommendSecondaryKey.

The primary and secondary Admin keys can be used for all API operations while the primary and secondary Recommend keys can only be used to get recommendations, typically used in client applications or websites requesting recommendations.

Sample authentication header:

x-api-key: <yourKeyGoesHere>

Train a New Model

POST /api/models

Starts the process of training a new model that could be later used to query for recommendations. To start training a model one must first upload usage data, and optionally a catalog and evaluation files, to the new blob storage account created by the preconfigured solution.

Training a new model is an asynchronous operation. The response to this HTTP request contains a Location header that reference the newly created model, as well as the model in the body of the response. You can use the Get Model API to query for the model training status along with other model information.

Optionally model metrics are computed if evaluationUsageRelativePath is provided. See Model Evaluation for more details.

The request body should be a valid Model Training Parameters JSON object.

The response body will contain a Model JSON object.

Sample Request:

POST https://<service_name>.azurewebsites.net/api/models
x-api-key: your_api_key
Content-Type: application/json

{
   "description": "Simple recommendations model",
   "blobContainerName": "input-files",
   "usageRelativePath": "booksTrainUsage",
   "catalogFileRelativePath": "books.csv",
   "evaluationUsageRelativePath": "booksTestUsage",
   "supportThreshold": 6,
   "cooccurrenceUnit": "User",
   "similarityFunction": "Jaccard",
   "enableColdItemPlacement": true,
   "enableColdToColdRecommendations": false,
   "enableUserAffinity": true,
   "allowSeedItemsInRecommendations": true,
   "enableBackfilling": true,
   "decayPeriodInDays": 30
}

Sample Response:

HTTP/1.1 201 Created
Content-Type: application/json; charset=utf-8
Location: https://<service_name>.azurewebsites.net/api/models/e16198c0-3a72-4f4d-b8ab-e4c07c9bccdb

{
   "id":"e16198c0-3a72-4f4d-b8ab-e4c07c9bccdb",
   "description": "Simple recommendations model",
   "creationTime":"2017-05-04T00:26:06.386324Z",
   "modelStatus":"Created"
}

If you would like to train a "Frequently Bought Together" model:

Set “enableUserAffinity” to false since you don’t need the time of the event or the event type to be used as inputs into the recommendation request.
Set the “supportThreshold” based on the minimum number of co-occurrences that you want items to appear together in a transaction to be used in the model.
Set “cooccurrenceUnit”. You can set to “Timestamp” if you want baskets to be modelled based on transactions that occurred at the same time in the check-out. If that is too strict, you can set to “User”.
Set the “similarityFunction” desired. We would recommend starting with Jaccard.

Catalog File Schema

The catalog file contains information about the items you are offering to your customer. The catalog data should follow the following format:

Without features - <Item Id>,<Item Name>,<Item Category>[,<Description>]
With features - <Item Id>,<Item Name>,<Item Category>,[<Description>],<Features list>

Sample Rows in a Catalog File

Without features:

AAA04294,Office Language Pack Online DwnLd,Office
AAA04303,Minecraft Download Game,Games
C9F00168,Kiruna Flip Cover,Accessories

With features:

AAA04294,Office Language Pack Online DwnLd,Office,, softwaretype=productivity, compatibility=Windows
BAB04303,Minecraft DwnLd,Games, softwaretype=gaming,, compatibility=iOS, agegroup=all
C9F00168,Kiruna Flip Cover,Accessories, compatibility=lumia,, hardwaretype=mobile

Schema details

Name	Mandatory	Type	Description
Item Id	Yes	[A-z], [a-z], [0-9], [_] (Underscore), [-] (Dash) Max length: 450	Unique identifier of an item.
Item Name	Yes	Any alphanumeric characters Max length: 255	Item name.
Item Category	Yes	Any alphanumeric characters Max length: 255	Category to which this item belongs (e.g. Cooking Books, Drama…); can be empty.
Description	No, unless features are present (but can be empty)	Any alphanumeric characters Max length: 4000	Description of this item.
Features list	No	Any alphanumeric characters Max length: 4000; Max number of features:20	Comma-separated list of feature name=feature value that can be used to enhance model recommendation.

Why add features to the catalog?

The recommendations engine creates a statistical model that tells you what items are likely to be liked or purchased by a customer. When you have a new product that has never been interacted with it is not possible to create a model on co-occurrences alone. Let's say you start offering a new "children's violin" in your store, since you have never sold that violin before you cannot tell what other items to recommend with that violin.

That said, if the engine knows information about that violin (i.e. It's a musical instrument, it is for children ages 7-10, it is not an expensive violin, etc.), then the engine can learn from other products with similar features. For instance, you have sold violin's in the past and usually people that buy violins tend to buy classical music CDs and sheet music stands. The system can find these connections between the features and provide recommendations based on the features while your new violin has little usage.

Features are imported as part of the catalog data. The SAR algorithm that is used to train the model will automatically detect the strength of each of the features based on the transaction data.

Features are Categorical

You should create features that resemble a category. For instance, price=9.34 is not a categorical feature. On the other hand, a feature like priceRange=Under5Dollars is a categorical feature. Another common mistake is to use the name of the item as a feature. This would cause the name of an item to be unique so it would not describe a category. Make sure the features represent categories of items.

How many/which features should I use?

You should use less than 20 features.

When are features actually used?

Features are used by the model when there is not enough transaction data to provide recommendations on transaction information alone. So features will have the greatest impact on “cold items” – items with few transactions. If all your items have sufficient transaction information you may not need to enrich your model with features.

Usage Data

A usage file contains information about how those items are used, or the transactions from your business.

Usage Events File Schema

A usage file is a CSV (comma separated value) file where each row in a usage file represents an interaction between a user and an item. Each row is formatted as follows:
<User Id>,<Item Id>,<Time>[,<Event Type> | ,,<Custom Event Weight>]

Name	Mandatory	Type	Description
User Id	Yes	[A-z], [a-z], [0-9], [_] (Underscore), [-] (Dash) Max length: 255	Unique identifier of a user.
Item Id	Yes	[A-z], [a-z], [0-9], [_] (Underscore), [-] (Dash) Max length: 450	Unique identifier of an item.
Time	Yes	Date in ISO 8601 format: yyyy-MM-ddTHH:mm:ss (e.g. 2017-04-20T18:00:00)	The time of the event
Event Type *only used in model evaluation	No	One of the following: Click (=weight of 1) RecommendationClick (=weight of 2) AddShopCart (=weight of 3) RemoveShopCart (=weight of -1) Purchase (=weight of 4) (defaults to Click)	The type of transaction. This will be used to determine the event strength. used only if Custom Event Weight* is not provided.
Custom Event Weight *only used in model evaluation	No	number	The trasaction strength. if provided, Event Type* will be ignored.

Sample Rows in a Usage File

00037FFEA61FCA16,288186200,2015-08-14T11:02:52,Click 
0003BFFDD4C2148C,297833400,2015-08-14T11:02:50,Purchase 
0003BFFDD4C2118D,297833300,2015-08-14T11:02:40,,5.2 
00030000D16C4237,297833300,2015-08-14T11:02:37,Purchase 
0003BFFDD4C20B63,297833400,2015-08-14T11:02:12,Click 
00037FFEC8567FB8,297833400,2015-08-14T11:02:04

Get Model Information

GET /api/models/{modelId}

Returns the model information, including the training status, parameters used to build the model, the time of creation, statistics about the model and evaluation results (See Model JSON Schema).

Sample Request:

GET https://<service_name>.azurewebsites.net/api/models/e16198c0-3a72-4f4d-b8ab-e4c07c9bccdb
x-api-key: your_api_key

Sample Response:

HTTP/1.1 200 OK
Content-Type: application/json; charset=utf-8

{
   "id":"e16198c0-3a72-4f4d-b8ab-e4c07c9bccdb",
   "description": "Simple recommendations model",
   "creationTime":"2017-05-04T00:26:06.386324Z",
   "modelStatus": "Completed",
   "modelStatusMessage": "Model Training Completed Successfully",
   "parameters": {
       ...
   },
   "statistics": {
       ...
   }
}

List Models

GET /api/models

Returns the id, description, creation time and status of all the existing models. (See Model JSON Schema).

Sample Request:

GET https://<service_name>.azurewebsites.net/api/models
x-api-key: your_api_key

Sample Response:

HTTP/1.1 200 OK
Content-Type: application/json; charset=utf-8

[{
   "id":"e16198c0-3a72-4f4d-b8ab-e4c07c9bccdb",
   "description": "Simple recommendations model",
   "creationTime":"2017-05-04T00:26:06.386324Z"
   "modelStatus": "Completed"
},
{
   "id": "1bd76c6d-0a25-4d9a-8111-9e897823ae1f",
   "description": "my other model",
   "creationTime": "2017-05-04T00:26:51.1024762Z",
   "modelStatus": "Created"
}]

Delete a Model

DELETE /api/models/{modelId}

Deletes the specified model.

Important If you delete the default model then there will be no default model until it is set again. Any recommendation requests to the default model will fail.

Sample Request:

DELETE https://<service_name>.azurewebsites.net/api/models/e16198c0-3a72-4f4d-b8ab-e4c07c9bccdb
x-api-key: your_api_key

Sample Response:

HTTP/1.1 202 Accepted
Content-Length: 0

Get Item-To-Item Recommendations

GET /api/models/{modelId}/recommend

Gets item-to-item recommendations for the model specified.

Empty recommendations may be returned if none of the items are in the catalog or if the model did not have sufficient training data to provide recommendations for the item.

Request parameters

Request Parameter	Description	Valid Values	Default Value
itemId	Seed item Id	string
recommendationCount	Number of recommended items to return	1-100	10

The response body will be a JSON array of Recommendation Objects.

Sample Request:

GET https://<service_name>.azurewebsites.net/api/models/e16198c0-3a72-4f4d-b8ab-e4c07c9bccdb/recommend?itemId=70322
x-api-key: your_api_key

Sample Response:

HTTP/1.1 200 OK
Content-Type: application/json; charset=utf-8

[{
  "recommendedItemId": "46846",
  "score": 0.45787626504898071
},
{
  "recommendedItemId": "46845",
  "score": 0.12505614757537842
},
...
{
  "recommendedItemId": "41607",
  "score": 0.049780447036027908
}]

Get Personalized Recommendations

POST /api/models/{modelId}/recommend

Gets personalized recommendations for the model specified, given a set of recent usage events for a particular user.

An optional user id may also be specified, in which case the usage events of that user, extracted during model training from the input usage files, will also be considered.

Note: If a user id is provided, additional usage events may still be provided in the request body, representing a more recent user activity

Request parameters

Request Parameter	Description	Valid Values	Default Value
userId	The id of a user to get recommendations for. The user id will be ignored unless the model was trained with a enableUserToItemRecommendations set to true. See Model Training Parameters Schema for more info.	string	null
recommendationCount	Number of recommended items to return	1-100	10

The request body must be an array of Get Recommendations Usage Events.

The response body will be a JSON array of Recommendation Objects.

Sample Request:

POST https://<service_name>.azurewebsites.net/api/models/e16198c0-3a72-4f4d-b8ab-e4c07c9bccdb/recommend?userId=user032669023
x-api-key: your_api_key

[
   {
      "itemId": "ItemId123",
      "eventType": "Click",
      "timestamp": "2017-01-31T23:59:59"
   },
   {
      "itemId": "ItemId456",
      "eventType": "Purchase"
   },
   {
      "itemId": "ItemId789",
      "weight": 2.3
   },
   {
      "itemId": "ItemId135"
   }
]

Sample Response:

HTTP/1.1 200 OK
Content-Type: application/json; charset=utf-8

[{
  "recommendedItemId": "46846",
  "score": 0.45787626504898071
},
{
  "recommendedItemId": "46845",
  "score": 0.12505614757537842
},
...
{
  "recommendedItemId": "41607",
  "score": 0.049780447036027908
}]

Set Default Model Id

PUT /api/models/default

Sets the specified model id as the default model.

Request parameters:

Request Parameter	Description	Valid Value
modelId	The model id to set as the default model	GUID

Sample Request:

PUT https://<service_name>.azurewebsites.net/api/models/default?modelId=e16198c0-3a72-4f4d-b8ab-e4c07c9bccdb
x-api-key: your_api_key

Sample Response:

HTTP/1.1 200 OK

Get Default Model Information

GET /api/models/default

To use this API, a default model must first be set

Returns the default model information, including the training status, parameters used to build the model, the time of creation, statistics about the model and evaluation results (See Model JSON Schema).

Sample Request:

GET https://<service_name>.azurewebsites.net/api/models/default
x-api-key: your_api_key

Sample Response:

HTTP/1.1 200 OK
Content-Type: application/json; charset=utf-8

{
   "id":"e16198c0-3a72-4f4d-b8ab-e4c07c9bccdb",
   "description": "Simple recommendations model",
   "creationTime":"2017-05-04T00:26:06.386324Z",
   "modelStatus": "Completed",
   "modelStatusMessage": "Model Training Completed Successfully",
   "parameters": {
       ...
   },
   "statistics": {
       ...
   }
}

Get Item-To-Item Recommendations from the Default Model

GET /api/models/default/recommend

To use this API, a default model must first be set

Gets item-to-item recommendations for the default model.

Empty recommendations may be returned if none of the items are in the catalog or if the model did not have sufficient training data to provide recommendations for the item.

Request parameters

Request Parameter	Description	Valid Values	Default Value
itemId	Seed item Id	string
recommendationCount	Number of recommended items to return	1-100	10

The response body will be a JSON array of Recommendation Objects. Request parameters

Request Parameter	Description	Valid Values	Default Value
itemId	Seed item Id	string
recommendationCount	Number of recommended items to return	1-100	10

Sample Request:

GET https://<service_name>.azurewebsites.net/api/models/default/recommend?itemId=70322
x-api-key: your_api_key

Sample Response:

HTTP/1.1 200 OK
Content-Type: application/json; charset=utf-8

[{
  "recommendedItemId": "46846",
  "score": 0.45787626504898071
},
{
  "recommendedItemId": "46845",
  "score": 0.12505614757537842
},
...
{
  "recommendedItemId": "41607",
  "score": 0.049780447036027908
}]

Get Personalized Recommendations from the Default Model

POST /api/models/default/recommend

To use this API, a default model must first be set

Gets personalized recommendations for the default model, given a set of recent usage events for a particular user. The request body should be an array of Get Recommendations Usage Events. The response body will be a JSON array of Recommendation Objects.

Sample Request:

POST https://<service_name>.azurewebsites.net/api/models/default/recommend
x-api-key: your_api_key

[
   {
      "itemId": "ItemId123",
      "eventType": "Click",
      "timestamp": "2017-01-31T23:59:59"
   },
   {
      "itemId": "ItemId456",
      "eventType": "Purchase",
   },
   {
      "itemId": "ItemId789",
      "weight": 2.3,
   },
   {
      "itemId": "ItemId135",
   }
]

Sample Response:

HTTP/1.1 200 OK
Content-Type: application/json; charset=utf-8

[{
  "recommendedItemId": "46846",
  "score": 0.45787626504898071
},
{
  "recommendedItemId": "46845",
  "score": 0.12505614757537842
},
...
{
  "recommendedItemId": "41607",
  "score": 0.049780447036027908
}]

Model Object Schema

The following table specifies the schema of the model JSON object:

Property Name	Type	Description
id	string	The model id
description	string	A textual description of the model, as provided when the model was created
creationTime	Date Time	The model creation UTC time and date
modelStatus	string	The status of the model: Created - the initial status of a new model InProgress - the model is being trained Completed - model trainining was completed successfully Failed - model trainining had failed
modelStatusMessage	string	An optional message associated with the model status
parameters	Model Training Parameters	The parameters used for training the model, as provided when the model was created
statistics	Model Training Statistics	The model training statistics, specifying metrics like training duration and model quality

Model Training Parameters Schema

The following table specifies the schema of the model training parameters JSON object (mandatory properties in bold):

Property Name	Mandatory?	Description	Type	Default Value
description	no	Textual description	string with max length of 256 characters	null
blobContainerName	yes	Name of the container where the catalog, usage data and evaluation data are stored. Note that this container must be in the storage account created by the preconfigured solution	string
usageRelativePath	yes	Relative path to either a virtual directory that contains the usage file(s) or a specific usage file to be used for training. See Usage events file format	string
catalogFileRelativePath	no	Relative path to the catalog file. See Catalog file format	string	null
evaluationUsageRelativePath	no	Relative path to either a virtual directory that contains the usage file(s) or to a specific usage file to be used for evaluation. See Usage events file format	string	null
supportThreshold	no	How conservative the model is. Number of co-occurrences of items to be considered for modeling.	3-50	6
cooccurrenceUnit	no	Indicates how to group usage events before counting co-occurrences. A 'User' cooccurrence unit will consider all items purchased by the same user as occurring together in the same session. A 'Timestamp' cooccurrence unit will consider all items purchased by the same user in the same time as occurring together in the same session.	User, Timestamp	User
similarityFunction	no	Defines the similarity function to be used by the model. Lift favors serendipity, Co-occurrence favors predictability, and Jaccard is a nice compromise between the two.	Cooccurrence, Lift, Jaccard	Jaccard
enableColdItemPlacement	no	Indicates if recommendations should also push cold items via feature similarity.	True, False	False
enableColdToColdRecommendations	no	Indicates whether the similarity between pairs of cold items (catalog items without usage) should be computed. If set to false, only similarity between cold and warm items will be computed, using catalog item features. Note that this configuration is only relevant when enableColdItemPlacement is set to true.	True, False	False
enableUserAffinity	no	For personalized recommendations, it defines whether the event type and the time of the event should be considered as input into the scoring.	True, False	False
enableBackfilling	no	Backfill with popular items when the system does not find sufficient recommendations.	True, False	True
allowSeedItemsInRecommendations	no	Allow seed items (items in the input or in the user history) to be returned as recommendation results.	True, False	False
decayPeriodInDays	no	The decay period in days. The strength of the signal for events that are that many days old will be half that of the most recent events.	Integer	30
enableUserToItemRecommendations	no	If true, userId is honored when requesting personalized recommendations. Training takes a bit longer when this is enabled.	True, False	False

Model Training Statistics Schema

The following table specifies the schema of the model training statistics JSON object:

Property Name	Type	Description
totalDuration	time span	The total duration of the model training process
trainingDuration	time span	The duration of the core model training
catalogParsing	Parsing Report Schema	The catalog file parsing report
usageEventsParsing	Parsing Report Schema	The usage events file(s) parsing report
numberOfCatalogItems	number	The total number of items found in the catalog file
numberOfUsers	number	The total number of unique users found in the usage events file(s)
numberOfUsageItems	number	The total number of valid (which are present in catalog if provided) unique items found in usage file(s)
catalogCoverage	number	The ratio of unique items found in usage file(s) and total items in catalog
evaluation	Model Evaluation Schema	The model evaluation metrics
catalogFeatureWeights	Catalog Feature Weights Schema	The calculated catalog feature's weights

Parsing Report Schema

The following table specifies the schema of the catalog\usage file(s) parsing report JSON object:

Property Name	Type	Description
duration	time span	The total parsing duration
errors	An array of Parsing Error Schema	A list of line parsing errors, if found
successfulLinesCount	number	The total number of lines parsed
totalLinesCount	number	The number of items with an unknown id

Parsing Error Schema

The following table specifies the schema of the parsing error JSON object:

Property Name	Type	Description
error	string	The type of the parsing error: MalformedLine - The line is in an invalid CSV format MissingFields - The line is missing some mandatory fields BadTimestampFormat - The time stamp field is malformed BadWeightFormat - The event weight field is not numeric MalformedCatalogItemFeature - Some catalog item feature has a malformed format ItemIdTooLong - The item id string is longer than the maximum allowed IllegalCharactersInItemId - The item id string contains invalid characters. UserIdTooLong - The user id string is longer than the maximum allowed IllegalCharactersInUserId - The user id string contains invalid characters. UnknownItemId - The item id doesn't appear in the catalog DuplicateItemId - The item id appears more than once in the catalog
count	number	The number of occurrences of this particular error
sample	Parsing Error Sample Schema	A sample of an occurrence of this particular error

Parsing Error Sample Schema

Property Name	Type	Description
file	string	The name of the file containing the parsing error
line	number	The line number of the parsing error

Model Evaluation Schema

The following table specifies the schema of the model evaluation JSON object:

Property Name	Type	Description
duration	time span	The total duration of the model evaluation process
usageEventsParsing	Parsing Report Schema	The evaluation usage events file(s) parsing report
metrics	Model Evaluation Metrics Schema	The model evaluation metrics

Model Evaluation Metrics Schema

The following table specifies the schema of the model evaluation metrics JSON object:

Property Name	Type	Description
precisionMetrics	Array of Precision Metric objects	Precision@K metrics for a model. These are a measure of quality of the Model. It works by splitting the input data into a test and training data. Then use the test period to evaluate what percentage of the customers would have actually clicked on a recommendation if k recommendations had been shown to them given their prior history.
diversityMetrics	Model Diversity Metrics	The model diversity metrics. Diversity gives customers a sense of how diverse the item recommendations are, based on their usage shown by bucket eg: 0-90, 90-99, 99-100. In simple terms, how many recommendations are coming from most popular items, how many from non-popular etc., unique items recommended.

Precision Metric Schema

The following table specifies the schema of the model precision metric JSON object:

Property Name	Type	Description
k	number	The value K used to calculate the metric values
percentage	number	Precision@K percentage
usersInTest	number	The total number of users found in the test dataset

Model Diversity Metrics Schema

The following table specifies the schema of the model diversity metric JSON object:

Property Name	Type	Description
percentileBuckets	Array of Diversity Percentile Bucket	The diversity metrics for all of the popularity buckets
totalItemsRecommended	number	Total number of items recommended (some may be duplicates)
uniqueItemsRecommended	number	The total number of distinct items that were returned for evaluation
uniqueItemsInTrainSet	The total number of distinct items in the train dataset

Diversity Percentile Bucket

The following table specifies the schema of the model diversity percentile bucket JSON object:

Property Name	Type	Description
min	number	The beginning percentile of the popularity bucket (inclusive)
max	number	The ending percentile of the popularity bucket (exclusive)
percentage	number	The fraction of all recommended users that belong to the specified popularity bucket

Catalog Feature Weights Schema

The following table specifies the schema of the catalog feature weights JSON object:

Property Name	Type	Description
feature name	string	Feature name as appeared in the catalog items features. See Feature List Schema
feature weight	number	The calculated weight of the feature. Features with higher absolute value indicate greater significance in the model when determining cold items correlations (cold items are catalog items that have no usage events). Negative weights indicate a reverse correlation, i.e. items that share the same value of a feature with a negative weight are considered less correlated

Get Recommendations Usage Event

The following table specifies the schema of the usage event JSON object used in get recommendations requests:

Property Name	Type	Description	Default Value
itemId	string	An item id to get recommendations for
timestamp	ISO 8601 format: yyyy-MM-ddTHH:mm:ss	The timestamp of event	Current UTC date and time
eventType	One of the following string values: Click (=weight of 1) RecommendationClick (=weight of 2) AddShopCart (=weight of 3) RemoveShopCart (=weight of -1) Purchase (=weight of 4)	The event type. This will determain the event strength only if weight is not provided	Click
weight	number	Custom event strength. If provided, eventType will be ignored	1.0

Recommendation Object Schema

The following table specifies the schema of the recommendation JSON object:

Property Name	Type	Description
recommendedItemId	string	The recommended item id
score	number	The score of this recommendation

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api-reference.md

File metadata and controls

API Reference

Swagger UI

Quick Reference

Authentication

Train a New Model

Catalog File Schema

Sample Rows in a Catalog File

Schema details

Why add features to the catalog?

Features are Categorical

How many/which features should I use?

When are features actually used?

Usage Data

Usage Events File Schema

Sample Rows in a Usage File

Get Model Information

List Models

Delete a Model

Get Item-To-Item Recommendations

Get Personalized Recommendations

Set Default Model Id

Get Default Model Information

Get Item-To-Item Recommendations from the Default Model

Get Personalized Recommendations from the Default Model

Model Object Schema

Model Training Parameters Schema

Model Training Statistics Schema

Parsing Report Schema

Parsing Error Schema

Parsing Error Sample Schema

Model Evaluation Schema

Model Evaluation Metrics Schema

Precision Metric Schema

Model Diversity Metrics Schema

Diversity Percentile Bucket

Catalog Feature Weights Schema

Get Recommendations Usage Event

Recommendation Object Schema