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Use this job when you want to compute user recommendations or item similarities using a Bayesian Personalized Ranking (BPR) recommender algorithm.
Default job nameCOLLECTION_NAME_bpr_item_recs
InputAggregated signals (the COLLECTION_NAME_recs_aggr collection by default)
OutputItems-for-item recommendations (the COLLECTION_NAME_bpr_item_recs collection by default) and Items-for-user recommendations (the COLLECTION_NAME_bpr_user_recs collection by default).
This job assumes that your signals collection contains the preferences of many users. It uses this collection of preferences to predict another user’s preference for an item that the user has not yet seen:
  • User. Use Training Collection User Id Field to specify the name of the user ID field, usually user_id_s.
  • Item. Use Training Collection Item Id Field to specify the name of the item ID field, usually item_id_s.
  • Interaction-value. Use Training Collection Counts/Weights Field to specify the name of the interaction value field, usually aggr_count_i.
BPR-based recommenders compare a pair of recommendations for a user.
You can also configure this job to read from or write to cloud storage. See Configure An Argo-Based Job to Access GCS and Configure An Argo-Based Job to Access S3.
Some jobs can be configured to read from or write to Google Cloud Storage (GCS).You can configure a combination of Solr and cloud-based input or output, that is, you can read from GCS and then write to Solr or vice versa. However, you cannot configure multiple storage sources for input or multiple storage targets for output; only one can be configured for each.See also Configure An Argo-Based Job to Access S3.

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Supported jobs

This procedure applies to these Argo jobs:
  • Content based Recommender
  • BPR Recommender
  • Classification
  • Evaluate QnA Pipeline
  • QnA Coldstart Training
  • QnA Supervised Training
For Spark jobs, see Configure A Spark-Based Job to Access Cloud Storage.

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How to configure a job to access GCS

  1. Gather the access key for your GCS account.
    See the GCS documentation.
  2. Create a Kubernetes secret: 
    kubectl create secret generic my-gcs-serviceaccount-key --from-file=<my-gcs-serviceaccount-key>.json --namespace <fusion-namespace>
  3. In the job’s Cloud storage secret name field, enter the name of the secret for the GCS target as mounted in the Kubernetes namespace.
    This is the name you specified in the previous step. In the example above, the secret name is my-gcs-serviceaccount-key.
    You can also find this name using kubectl get secret -n <fusion-namespace>.
  4. In the job’s Additional Parameters, add this parameter:
    • Parameter name: google.cloud.auth.service.account.json.keyfile
    • Parameter value: <name of the keyfile that is available when the GCS secret is mounted to the pod>
      The file name may be different than the secret name. You can check using kubectl get secret -n <fusion-namespace> <secretname> -o yaml.
Some jobs can be configured to read from or write to Amazon S3 (S3).You can configure a combination of Solr and cloud-based input or output, that is, you can read from S3 and then write to Solr or vice versa. However, you cannot configure multiple storage sources for input or multiple storage targets for output; only one can be configured for each.See also Configure An Argo-Based Job to Access GCS.

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Supported jobs

This procedure applies to these Argo jobs:
  • Content based Recommender
  • BPR Recommender
  • Classification
  • Evaluate QnA Pipeline
  • QnA Coldstart Training
  • QnA Supervised Training
For Spark jobs, see Configure A Spark-Based Job to Access Cloud Storage.

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How to configure a job to access S3

  1. Gather the access key and secret key for your S3 account.
    See the AWS documentation.
  2. Create a Kubernetes secret: 
    kubectl create secret generic aws-secret --from-literal=my-aws-key-file='<access key>' --from-literal=my-aws-secret-path='<secret key>' --namespace <fusion-namespace>
  3. In the job’s Cloud storage secret name field, enter the name of the secret for the S3 target as mounted in the Kubernetes namespace.
    This is the name you specified in the previous step. In the example above, the secret name is aws-secret.
    You can also find this name using kubectl get secret -n <fusion-namespace>.
  4. In the job’s Additional Parameters, add these two parameters:
    • Param name: fs.s3a.access.keyPath
      Param value: <name of the file containing the access key that is available when the S3 secret is mounted to the pod>
    • Param name: fs.s3a.secret.keyPath
      Param value: <name of the file containing the access secret that is available when the S3 secret is mounted to the pod>
      The file name may be different than the secret name. You can check using kubectl get secret -n <fusion-namespace> <secretname> -o yaml.
BPR collaborative recommendations dataflow BPR dataflow
If using Solr as the training data source, ensure that the source collection contains the random_* dynamic field defined in its managed-schema.xml. This field is required for sampling the data. If it is not present, add the following entry to the managed-schema.xml alongside other dynamic fields <dynamicField name="random_*" type="random"/> and <fieldType class="solr.RandomSortField" indexed="true" name="random"/> alongside other field types.

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Tuning tips

The BPR Recommender job has a few unique tuning parameters compared to the ALS Recommender job:
  • Training Data Filtered By Popular Items. By setting the minimum number of user interactions required for items to be included in training and recommendations, you can suppress items that do not yet have enough signals data for meaningful recommendations.
  • Filter already clicked items. This feature produces only “fresh” recommendations, by omitting items the user has already clicked. (It also increases the job’s running time.)
  • Perform approximate nearest neighbor search. This option reduces the job’s running time significantly, with a small decrease in accuracy. If your training dataset is very small, then you can disable this option.
  • Evaluate on test data. This feature samples the original dataset to evaluate how well the trained model predicts unseen user interactions. The clicks that are sampled for testing are not used for training. For example, with the default configuration, users who have at least three total clicks are selected for testing. For each of those users, one click is used for testing and the rest are used for training. The trained model is applied to the test data, and the evaluation results are written to the log.
  • Metadata fields for item-item evaluation. These fields are used during evaluation to determine whether pairs belong to the same category.

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Query pipeline setup

You can use the Fusion UI and the Objects API to migrate collections and related objects, including your searchable data, configuration data, pipelines, aggregations, and other objects on which your collections depend. You can also migrate entire apps.
To upgrade from one Fusion version to a 5.x version, see Fusion 5 Upgrades to migrate objects.
You might need to migrate objects in the following circumstances:
  • When migrating data from development environments into testing and production environments
  • To back up data, so you can restore it after an incident of data loss
  • When the migrator script was not able to migrate an object automatically
When you export and import objects to migrate them, make sure you that any data gets to where it is going before deleting the sources.

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Migration approaches

Several approaches are available for migrating Fusion objects. This table summarizes the approaches.
Export an appImport an appExport an objectImport an objectAdd an object to an app
Fusion UIApp configurationLauncher
(entire app) App configuration
(combine apps)		--Object Explorer
Objects API
(endpoints)		GET from export endpointPOST to import endpointGET from export endpointPOST to import endpoint-
For more information about using the Objects API to export and import objects, see Objects API.The remainder of this topic describes approaches in the Fusion UI.Use the parts of the Fusion UI indicated in the table to export and import apps and specific objects. Exporting creates a zip file. To import, you select a data file and possibly a variable file.The approach with Object Explorer differs. With Object Explorer, you can add objects from other apps (or that are not linked to any apps) to the currently open app.

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Export an app with the Fusion UI

How to export an app with the Fusion UI
  1. Navigate to the launcher.
  2. Hover over the app you want to export and click the Configure icon: App config button
  3. In the app config window, click Export app to zip:
See import for information to import the downloaded zip file into other instances of Fusion 5.x.

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Import an app with the Fusion UI

How to import an app with the Fusion UI
  1. Navigate to the launcher.
  2. Click Import app.
  3. Under Data File, click Choose File and select the zip file containing the app you want to import.
  4. If your app has usernames and passwords in a separate file, select it under Variables File. If the Variables File is needed, it must be a separate file that is not in a .zip file. It is a .json map of variables to values. The following is an example: 
    {
"secret.dataSources.Inventory_BR_S3_DS.password":"SOMETHING",
"secret.dataSources.LLM_A_BR_S3_DS.password":"FmJSaDE9Tj5REDACTED",
"secret.dataSources.LLM_BR_Inventory_S3_DS.password":"FmJSaDE9Tj5GzIVvethAC4Huh",
"secret.dataSources.LLM_BR_Load_S3_DS.password":"FmJSaDE9Tj5GzIVvethAC4"
}
  5. You can sometimes edit parameter values to use the new values in the imported app. If this is the case, Fusion displays a dialog box that lets you edit the parameter values. Edit parameter values Make desired changes, and then click Import.

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Copy an app

To copy an app from one deployment to a different one, export the app on the source deployment, and then import the app on the target deployment.

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Import objects into an app

You can import objects into the currently open app.How to import objects into an open app
  1. In the Fusion launcher, click the app into which you want to import objects. The Fusion workspace appears.
  2. Click System > Import Fusion Objects. The Import Fusion Objects window opens. Import Objects Window
  3. Select the data file from your local filesystem. If you are importing usernames and passwords in a separate file, select it under Variables File.
  4. Click Import. If there are conflicts, Fusion prompts you to specify an import policy: Import Conflicts
    • Click Overwrite to overwrite the objects on the target system with the ones in the import file.
    • Click Merge to skip all conflicting objects and import only the non-conflicting objects.
    • Click Start Over to abort the import. Fusion confirms that the import was successful: Successful Import
  5. Click Close to close the Import Fusion Objects window.

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Add an object to an app

You can add objects present in other apps (or in no apps) to the open app. Some objects are linked to other apps. You can also add those directly to an app.
  • Add an object to an app – While in the Fusion workspace for the app to which you want to add an object, open Object Explorer and click In Any App. Search for or browse to the object you want to add. Hover over the object, click the App App icon, and then click Add to this app. Add to this app
  • Add an object to an app directly – In cases when an object is linked to an app, but is not linked directly to the app (it is linked via some dependency), you can add the object to an app directly. While in the Fusion workspace for the app to which you want to add an object directly, open Object Explorer and click In Any App. Search for or browse to the object you want to add. Hover over the object, click the App App icon, and then click Add to this app directly. Add to this app directly
In Managed Fusion, you can download and import a query pipeline that works out of the box with the default BPR Recommender job.There are two separate pipelines attached below which work in different ways to query and return recommendations.Query for recommendations only
  1. Download the APPName_item_item_rec_pipelines_bpr.json file.
  2. Rename the file to replace APPName with the name of your Managed Fusion app, such as ProductCatalog_item_item_rec_pipelines_bpr.json.
  3. Open the JSON file, replace all instances of APPName with the name of your Managed Fusion app, such as ProductCatalog, and save it.
  4. Import the JSON file into your Managed Fusion instance using the Query Pipelines REST API: 
    curl -u USERNAME:PASSWORD <https://EXAMPLE_COMPANY.b.lucidworks.cloud/api/query-pipelines -XPOST -H 'content-type:application/json' -d@<path/to/filename.json>
  5. In the Managed Fusion UI, navigate to Query > Query Pipelines to verify that the new pipeline is available.
    This pipeline should be used to query the collection where the recommendations are stored. It makes a query against the itemId field and only returns the recommended itemId values. To get the actual items, you need to make a second query to the respective catalog collection with the returned itemId values.
No additional configuration is needed to use this pipeline with the default BPR job configuration.Boost recommended products from catalog
  1. Download the APPName_items_for_item_bpr_boost.json file
  2. Rename the file to replace APPName with the name of your Managed Fusion app, such as ProductCatalog_item_item_rec_pipelines_bpr.json.
  3. Open the JSON file, replace all instances of APPName with the name of your Managed Fusion app, such as ProductCatalog.
  4. Fill in the collection name field in the first Recommend Items for Item stage and save the file.
  5. Import the JSON file into your Managed Fusion instance using the Query Pipelines REST API: 
    curl -u USERNAME:PASSWORD https://EXAMPLE_COMPANY.b.lucidworks.cloud/api/query-pipelines -XPOST -H 'content-type:application/json' -d@<path/to/filename.json>
  6. In the Managed Fusion UI, navigate to Query > Query Pipelines to verify that the new pipeline is available.
This pipeline queries the recommendations collection and then makes a subsequent query to the actual catalog collection boosting the recommended items and returning the actual items from the catalog. This pipeline will therefore also return recommendations even if none were generated/available.
This pipeline expects a request parameter called id=<itemId> to be appended to the request in order to work. An example query URL to this pipeline would look like https://EXAMPLE_COMPANY.b.lucidworks.cloud/api/query-pipelines/APPName_items_for_item_bpr_boost/collections/catalog/select?q=**:**&id=SomeItemId
If the pipeline does not appear in the Query Pipelines panel, you may need to attach it to your app like this: Go to System > Object Explorer, click the In No Apps filter, hover over the pipeline, click the oe-app-menu icon, and select Add to this app.
No additional configuration is needed to use this pipeline with the default BPR job configuration.See also
You can download and import a query pipeline that works out of the box to fetch items-for-user recommendations generated by the default BPR Recommender job.There are two separate pipelines attached below which work in different ways to query and return recommendations.Query for recommendations only
  1. Download the APPName_item_user_rec_pipelines_bpr.json file.
  2. Rename the file to replace APPName with the name of your Fusion app, such as ProductCatalog_item_item_rec_pipelines_bpr.json.
  3. Open the JSON file, replace all instances of APPName with the name of your Fusion app, such as ProductCatalog, and save it.
  4. Import the JSON file into your Fusion instance using the Query Pipelines REST API: 
    curl -u USERNAME:PASSWORD <fusion_proxy>/api/query-pipelines -XPOST -H 'content-type:application/json' -d@<path/to/filename.json>
  5. In the Fusion UI, navigate to Query > Query Pipelines to verify that the new pipeline is available.
    This pipeline should be used to query the collection where the recommendations are stored. It makes a query against the userId field and only returns the recommended itemId values. To get the actual items, you need to make a second query to the respective catalog collection with the returned itemId values.
No additional configuration is needed to use this pipeline with the default BPR job configuration.Boost recommended products from catalog
  1. Download the APPName_items_for_user_bpr_boost.json file
  2. Rename the file to replace APPName with the name of your Fusion app, such as ProductCatalog_items_for_user_bpr_boost.json.
  3. Open the JSON file, replace all instances of APPName with the name of your Fusion app, such as ProductCatalog.
  4. Fill in the collection name field in the first Recommend Items for User stage and save the file.
  5. Import the JSON file into your Fusion instance using the Query Pipelines REST API: 
    curl -u USERNAME:PASSWORD <fusion_proxy>/api/query-pipelines -XPOST -H 'content-type:application/json' -d@<path/to/filename.json>
  6. In the Fusion UI, navigate to Query > Query Pipelines to verify that the new pipeline is available.
This pipeline queries the recommendations collection and then makes a subsequent query to the actual catalog collection boosting the recommended items and returning the actual items from the catalog. This pipeline will therefore also return recommendations even if none were generated/available.
This pipeline expects a request parameter called id=<userId> to be appended to the request in order to work. An example query URL to this pipeline would look like https://example-recs-fusion.com/api/query-pipelines/APPName_items_for_user_bpr_boost/collections/catalog/select?q=**:**&id=SomeUserId
If the pipeline does not appear in the Query Pipelines panel, you may need to attach it to your app like this: Go to System > Object Explorer, click the In No Apps filter, hover over the pipeline, click the oe-app-menu icon, and select Add to this app.
See also

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Configuration properties

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