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Items-for-item recommendations present items that are similar to a specified item. For example, when the user is viewing a BMX bicycle, Fusion can recommend other BMX bicycles. Similarity can be based on different criteria, such as click patterns, people who bought this also bought that, percentage match of document tags, and so on.

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Collaborative items-for-item recommendations

If you have enabled signals and recommendations for a collection, then the default COLLECTION_NAME_bpr_item_recs, a BPR Recommender job is already created and configured to produce items-for-item recommendations (as well as items-for-user recommendations). This is the recommended job type, and produces better results with a short runtime. To create the query pipeline that consumes this job’s output, see Fetch Items-for-Item Recommendations (Collaborative/BPR Method).
In 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 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:
  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 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 Fusion app, such as ProductCatalog_items_for_item_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 Item stage and save the file. 5. Import the JSON file into your Fusion instance using the Query Pipelines REST API:
  5. 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=<itemId> 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_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
If you want to use different parameters for items-for-item recommendations and items-for-user recommendations, simply create separate jobs for each, where one job configuration includes an output collection for items-for-item recommendations only and the other includes an output collection for items-for-user recommendations only.

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Content-based items-for-item recommendations

The Content-Based Recommender job computes item similarities based on their content, such as product descriptions. This is a useful option when you do not have enough signals for accurate results using the collaborative method described above. To create the query pipeline that consumes this job’s output, run the recommender job and then see Fetch Items-for-Item Recommendations (Content-Based Method).
In Fusion, you can download and import a query pipeline that works out of the box to fetch the items-for-item recommendations generated by the default Content-Based 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_content.json file.
  2. Rename the file to replace APPName with the name of your Fusion app, such as ProductCatalog_item_item_rec_pipelines_content.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:
  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 itemId field and 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_content_boost.json file
  2. Rename the file to replace APPName with the name of your Fusion app, such as ProductCatalog_content_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 Item stage and save the file.
  5. Import the JSON file into your Fusion instance using the Query Pipelines REST API:
  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=itemId 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=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.
See also