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      Machine Learning Jobs

      Fusion AI provides these job types to perform machine learning tasks.

      Signals analysis

      These jobs analyze a collection of signals in order to perform query rewriting, signals aggregation, or experiment analysis.

      • Ground Truth

        Estimate ground truth queries using click signals and query signals, with document relevance per query determined using a click/skip formula.

      Query rewriting

      These jobs produce data that can be used for query rewriting or to inform updates to the synonyms.txt file.

      • Head/Tail Analysis

        Perform head/tail analysis of queries from collections of raw or aggregated signals, to identify underperforming queries and the reasons. This information is valuable for improving overall conversions, Solr configurations, auto-suggest, product catalogs, and SEO/SEM strategies, in order to improve conversion rates.

      • Token and Phrase Spell Correction

        Detect misspellings in queries or documents using the numbers of occurrences of words and phrases.

      Signals aggregation

      Experiment analysis

      • Ranking Metrics

        Calculate relevance metrics (nDCG and so on) by replaying ground truth queries against catalog data using variants from an experiment.

      • SQL-Based Experiment Metric (deprecated)

        This job is created by an experiment in order to calculate an objective.

        This job is deprecated as of Fusion AI 4.0.2.

      Collaborative recommenders

      These jobs analyze signals and generate matrices used to provide collaborative recommendations.

      Content-based recommenders

      Content-based recommenders create matrices of similar items based on their content.

      Content analysis