How To
    Learn More

      Getting Started with Recommendations and Boosting

      Signals provide the data that Fusion uses to generate collaborative recommendations. The simplest way to get started is to enable signals and recommendations in one of your primary collections.

      Once you do this, Fusion automatically creates a set of default objects and begins creating and updating collections of recommendations on a regular schedule.

      Content-based recommendations can be used without enabling signals or recommendations, but they require manual configuration.

      Enabling signals

      Signals are enabled by default for new collections when you have a Fusion AI license installed. You can enable or disable signals for any collection at Collections > Collections Manager.

      Enable Signals

      Enabling signals automatically creates a set of aggregation jobs which create the input data for recommendations. See Signals and Aggregations for complete details.

      Enabling recommendations

      Recommendations are not enabled by default; you can do this at Collections > Collections Manager.

      Enable Recommendations

      When you enable recommendations, this automatically enables the items-for-user and items-for-item recommendation methods. To use additional recommendation methods, you must configure them separately.

      Default objects for recommendations

      When recommendations are enabled, Fusion automatically creates a default set of collections, jobs, schedules, and query pipelines that provide basic functionality for recommendations.

      You can tune the default jobs and pipelines as needed to refine the results, or create new ones, then configure your search application to request recommendations from the query pipelines.

      See also the default objects created when you enable signals. These must already exist when you enable recommendations.


      • <collection>_items_for_item_recommendations

        Collection to hold generated item-item similarities (by default 10 per item). No user_id_s data is present. A Recommend Items for Item query pipeline stage can use the similarities to return item recommendations. For example, a query in which doc_id_s = docA would return an ordered list of other doc_id_s values for documents that are similar to document docA, along with the similarities. For example: [("docB", 0.83), ("docC", 0.55), ("docD", 0.43), …​, ("docK", 0.22)].

      • <collection>_items_for_user_recommendations

        Collection to hold recommended items for a user. By default the job creates 10 recommendations per user.

      Job and schedule

      Enabling recommendations creates one new ALS Recommender job, which consumes the output of the signals aggregation jobs.



      Default input collection


      Default output collections

      <collection>_items_for_user_recommendations <collection>_items_for_item_recommendations

      Default trigger

      None; schedule or start this job manually.

      As suggested by the output collection names, this default job produces recommender data for items-for-user and items-for-item recommendations.

      The <collection>_user_item_preferences_aggregation job provides input data for this job and must run before it. See SQL Aggregations for details.

      Fusion does not automatically create a Query-to-Query Similarity job, which is needed for certain recommender types.

      Query pipelines

      • <collection>_items_for_user_recommendations

        Query pipeline to generate recommendations of items for a user.

      • <collection>_items_for_item_recommendations

        Query pipeline to generate recommendations of items similar to an item.

        Default items-for-item recommendations pipeline

      • <collection>_queries_query_recs

        Query pipeline to generate queries-for-query recommendations using a model created by the Query-to-Query Session-Based Similarity job.