Similarity solutions

  • Release version: Australia
  • Updated March 12, 2026
  • 2 minutes to read
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    Summary of Similarity solutions

    Similarity solutions in ServiceNow use Machine Learning (ML) to compare text fields in resolved alert records with those in open alert records, enabling you to reuse resolution approaches efficiently. This capability helps streamline alert management by suggesting resolutions based on previously resolved similar alerts.

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    Training a Similarity Solution

    • Training involves compiling a collection of words from key alert fields—such as Short Description, Description, Source, Type, Resource, and Metric Name—to compare text between resolved and open alerts.
    • Your training data filter must return at least one record, with a preferred dataset size between 30,000 and 300,000 authentic, relevant, and current records.
    • Use relative date filters (e.g., last 3, 6, or 12 months) instead of hard-coded dates to ensure your training dataset stays current without manual updates.
    • Repeat training as necessary to refine the solution and achieve an acceptable similarity model.

    Fields to Include

    Select fields that are likely to contain meaningful words or phrases for identifying similar alerts. Ensure that similarity fields correspond logically to the record state; for example, exclude fields that won't be present in open alerts (like Close note) to maintain effective comparison.

    Similarity Score and Threshold

    • The similarity score ranges from 0 to 100 and quantifies how closely two alert records match.
    • Only alerts with a similarity score above your configured threshold are returned by the solution.
    • Use the Show training progress feature to review example similarity scores and adjust the threshold accordingly for optimal results.

    Training Progress and Performance

    • Training duration depends on the dataset size and number of classes; for example, training with 100,000 records may take approximately five hours.
    • Training process includes fetching training data, removing duplicates, training the ML model, and uploading the trained solution.
    • You can monitor these stages through the Show training progress option on the Solutions page to track progress and status.

    Similarity solutions enable you to use Machine Learning (ML) to compare the text in a resolved alert record to an open alert record to reuse its resolution approach.

    Training a similarity solution

    To train a similarity solution, you collect words to compile a collection that Machine Learning (ML) can use to compare text in the Short Description, Description, Source, Type, Resource, and Metric Name fields in a resolved alert to see whether the words in the set match words in an open alert. The resolved alert, which is similar to an open alert, provides an example to show how the open alert can be resolved.

    To train a solution, the filter must return at least one record. If your filter returns no records, update it.
    Note:
    The preferred number of records for training a solution is between 30,000 records and 300,000. If you submit more than 300,000 records, the most recent 300,000 records are used to train the solution. Use only authentic records from the database.
    • Ensure that the records you train are not too old and that they are relevant to your business needs. Keep the words in the collection current.
    • Do not use hard-coded dates as filters because these filters are not updated when you retrain solutions unless you update them manually before every retraining. Instead, use relative date filters, for example, the last 3 months, last 6 months, or last 12 months.
    • Perform training as needed until it provides an acceptable similarity solution. This practice provides you time to review and update your solution definition.

    For more information on similarity solution concept, see Create and train a similarity solution.

    Fields to include in the solution

    Record the fields that are likely to contain words and phrases that help the system identify similar records for your solution.

    The similarity fields that you select should be a subset of your input field selections. For example, if you select fields from incident records that are in Open state, do not select Close note as a similarity field. Because open records do not include Close note fields, the text cannot be similar.

    The similarity fields are available to users when they create records.

    About the similarity score

    The similarity score is a measure from 0-100 of the degree of similarity between two alert records. Alert records that have a similarity score higher than the threshold that you specify is returned by the solution.

    Review similarity examples and their scores using the Show training progress feature to determine whether to either increase or decrease the solution threshold. You can change the threshold value in the Threshold for Similarity Score field.

    View training solution progress

    Training times vary based on the number of records and classes within the training set. The more records and classes you use, the longer the training can take. For example, a data set containing 100,000 records and several hundred classes can take around five hours to complete.

    To show the training solution progress, the ML solution automatically performs the following activities when you select Show training progress on the Solutions page. For more information, see View solution training progress.
    Table 1. Solution training activities
    Activity Description
    Fetching files for training. The system downloads the training records and sends them to the nearest training service.
    Preparing the data. The system removes duplicate records from the training set.
    Training the solution. The training service trains the solution.
    Uploading the trained solution. The training service uploads the solution as attachment records.