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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_item_recommendations job is already created and configured to produce items-for-item recommendations (as well as items-for-user recommendations): Default recommendations job This is an ALS Recommender job.
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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Learn more

The BPR method is recommended and the ALS method is deprecated.
If you have enabled signals and recommendations for a collection, then the _items_for_item_recommendations query pipeline is created by default and configured to fetch items-for-item recommendations using the output from the ALS Recommender job. It is similar to the default query pipeline that fetches content from your main collection, but it has an additional Recommend Items for Item stage:Default items-for-item recommendations pipelineThis pipeline is a template that you can use two different ways:
  • Use only the Recommend Items for Item stage
    This method returns only the document IDs of the recommended items. Your search application must perform additional queries to retrieve the desired fields for those items, such as their names, images, categories, and so on.
  • Query Solr directly
    This method returns complete documents about the recommended items from the _items_for_item_recommendations collection. The fields that are included in these recommender documents are configured in the recommender job’s itemMetadataJoinField Item Metadata Join Field and itemMetadataFields Item Metadata Fields fields.

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Fetching recommendations from App Studio

App Studio can only access user-created collections; it cannot access system collections such as the default collections that Fusion creates for recommendations. If you are using App Studio to create your front-end search application, you must:
  • Create a new collection for item-for-item recommendations
  • Configure the recommender job to send output to the user-created collection instead of the system collection
  • Direct your queries to the user-created collection
Once this is done, you can fetch recommendations as usual, using either of the methods explained below.

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Fetching with the Recommend Items for Item stage

With this method, we use only one query pipeline stage: the Recommend Items for Item query stage. This stage works with the output from the ALS Recommender job.This method returns only the document IDs of the recommended items. Your search application must perform additional queries to retrieve the desired fields for those items, such as their names, images, categories, and so on.How to fetch recommendations using the Recommend Items for Item stage
  1. From your _items_for_item_recommendations collection, navigate to Querying > Query Workbench.
If this collection is empty, you must run one of the items-for-item recommender jobs.
  1. Click Load… and open the _items_for_item_recommendations query pipeline, if it is not open already.
  2. Disable all of the pipeline stages except Recommend Items for Item.
  3. Verify that the following fields are correctly configured in the Recommend Items for Item query stage:
    • numRecommendations/Number of Recommendations
      This is the number of recommendations to return. It should be less than or equal to the value of the numSims/Number of Item Similarities to Compute parameter in the model tuning parameters of the COLLECTION_NAME_item_recommendations job configuration.
    • modelID/Model ID
      This must match the modelId/Recommender Model ID value in the recommender job’s model settings.
    • collection/Recommendation Collection
      This should be the collection specified in the outputItemSimCollection/Item-to-item Similarity Collection parameter of the recommender job’s Input/output settings.
    • resultsLocation/Results Location
      Select the As Response value for this field.
    • There are several fields that specify the names of fields that should be present in the documents in the _items_for_item_recommendations collection. Verify that these values match the field names in that collection.
    • To use live signals in addition to historical signals, enable Estimate Recent Results.
      Be sure to click Apply after changing any of the stage configuration parameters.
      At this point, the results panel should display “No Search Results”. This is normal; we will see results at a later step.
  4. Click Save.
Save the modified pipeline as a new pipeline with a different name, to distinguish it from the default pipeline.
  1. Test the pipeline configuration:
    1. Select an item from your main collection to use for testing.
      1. Navigate to your main collection and open the Query Workbench.
      2. Search for an item and copy the value of its id field.
      3. Return to the _items_for_item_recommendations collection and the Query Workbench.
    2. Click Parameters.
    3. Click Edit Parameters.
      The Parameters and Values window appears.
    4. Click the Add Add icon icon.
    5. Enter the parameter name item_id and the value that you copied from the id field of the item in your main collection.\ item_id query parameter
    6. Click Close to close the Parameters and Values window.
    7. In the lower right, select View As: JSON: View as JSON Notice that the search results contain only a document ID and a weight, as you can see when you expand the items field and any of its Object fields: Recommend Items for Item query stage
If the items field is empty, choose another item from your main collection and update the item_id query parameter to match it. Some items in your main collection may have no recommendations.
  1. Get the query URI that your search application can use to retrieve recommendations from your modified pipeline:
    1. Click URI.
      The Query Workbench displays a Working URI and a Published URI.\ Query URI
    2. Click the Published URI to copy it to your clipboard.
      This is the URI and parameters that your search application should use to query for additional recommendations.
Tips:
  • Each time you query for recommendations, replace the item_id parameter value with the ID of the item for which you want recommendations.
  • Replace the Fusion hostname as needed, depending on your production environment.
  • As shown above, Fusion returns an array of document IDs and weights. Your search application must then query the main collection to retrieve the details about each of the recommended documents. That is, for each docId value returned from the recommendations collection, query for the corresponding id value in the main collection, and order the set of results according to the weight value from the recommendations collection.

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Querying Solr directly

With this method, we use only the Solr Query pipeline stage.This method returns only the document IDs of the recommended items. Your search application must perform additional queries to retrieve the desired fields for those items, such as their names, images, categories, and so on.How to fetch recommendations using the Solr Query stage
  1. From your _items_for_item_recommendations collection, navigate to Querying > Query Workbench.
  2. Click Load.
  3. Select the _items_for_item_recommendations query pipeline.
  4. Disable all stages in the pipeline except the Solr Query stage.
  5. Click Save.
Save the modified pipeline as a new pipeline with a different name, to distinguish it from the default pipeline.
  1. Test the pipeline configuration:
    1. Select an item from your main collection to use for testing.
      1. Navigate to your main collection and open the Query Workbench.
      2. Search for an item and copy the value of its id field.
      3. Return to the _items_for_item_recommendations collection and the Query Workbench.
    2. In the search field, enter the parameter name itemId and the value that you copied from the id field of the item in your main collection.
      For example, enter itemId:10463303 (substituting the value from your main collection) and click the Search button.
      The number of results should be the same as the numRecs/Number of User Recommendations to Compute value in the _item_recommendations job (the default is 10).
    If the results panel displays “No Search Results”, choose another item from your main collection and update the item_id query parameter to match it. Some items in your main collection may have no recommendations.
    1. In any of the search results, click show fields.
      Notice these important fields:
      • itemId is the original item to which the recommendation pertains.
      • otherItemId is the recommended item.
      • sim is the similarity score, or the estimated likelihood that the recommended item is related to the original item.
  2. Get the query URI that your search application can use to retrieve recommendations from your modified pipeline:
    1. Click URI.
      The Query Workbench displays a Working URI and a Published URI.\ Query URI
    2. Click the Published URI to copy it to your clipboard.
      This is the URI and parameters that your search application should use to query for additional recommendations.
Tips:
  • Each time you query for recommendations, replace the itemId parameter value with the ID of the item for which you want recommendations.
  • Replace the Fusion hostname as needed, depending on your production environment.
  • As shown above, Fusion returns a set of results that each include an otherItemId and a sim score. Your search application must then query the main collection to retrieve the details about each of the recommended documents. That is, for each otherItemId value returned from the recommendations collection, query for the corresponding id value in the main collection, and order the set of results according to the sim value from the recommendations collection.
You can eliminate these additional queries by configuring the join field and item metadata fields in the _item_recommendations job’s Item metadata settings. This copies the specified metadata fields from the main collection into the recommendations collection so that they can be retrieved in a single query. For example, if you specify name, url, category, image, or similar fields in the job configuration, then those are returned in the recommender results without the need for additional queries.