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The Content-Based Recommender job is deprecated in Lucidworks Search 5.19.0 and is expected to be removed in 6.0.0. For more information, see Deprecations and Removals.
Use this job when you want to compute item similarities based on their content, such as product descriptions. First, item content is vectorized; different vectorization methods are available. Then, similar items are selected based on cosine similarity (“nearest neighbor”) between their vectors. At a minimum, you must specify these:
  • An ID for this job
  • The name of the training collection, that is, the collection with your content
  • An output collection; create a separate collection for this
  • The name of the ID field for documents in the training collection, such as item_id_s
  • The names of one or more content fields in the training collection

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Tuning tips

  • Configure Metadata fields for item-item evaluation to use those fields during evaluation to determine whether pairs belong to the same category.
  • Perform approximate nearest neighbor search is enabled by default to significantly reduce the job’s running time, with a small decrease in accuracy. If your training dataset is very small, then you can disable this option.
  • If your content contains a lot of domain-specific jargon, enable Use Word2Vec for vectorization.
  • If your documents are too short or too long, enable Use TF-IDF for vectorization.
  • If the job runs out of memory during encoding, reduce Batch size to compute encodings. The default is 1024. Halve the value until the job completes, for example 512, then 256, then 128. Smaller batches don’t reduce recommendation quality.

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Query pipeline setup

To create the query pipeline that consumes this job’s output, see Fetch Items-for-Item Recommendations (Content-Based Method).
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 Lucidworks Search 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 Lucidworks Search app, such as ProductCatalog, and save it.
  4. Import the JSON file into your Lucidworks Search instance using the Query Pipelines REST API:
  5. In the Lucidworks Search 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 Lucidworks Search app, such as ProductCatalog_content_boost.json.
  3. Open the JSON file, replace all instances of APPName with the name of your Lucidworks Search 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 Lucidworks Search instance using the Query Pipelines REST API:
  6. In the Lucidworks Search 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. An example query URL to this pipeline would look like https://EXAMPLE_COMPANY.b.lucidworks.cloud/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

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Configuration properties