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.

  • Phrase Extraction

    Identify multi-word phrases in signals.

  • 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

  • Cluster Labeling

    Use this job when you already have clusters or well-defined document categories, and you want to discover and attach keywords to see representative words within those existing clusters. (If you want to create new clusters, use the Document Clustering job.)

  • Collection Analysis

    Use this job when you want to compute basic metrics about your collection, like average word length, phrase percentages, and outlier documents (with very many or very few documents).

  • Document Clustering

    Cluster a set of documents and attach cluster labels.

  • Logistic Regression Classifier Training

    Train a regularized logistic regression model for text classification.

  • Outlier Detection

    Use this job when you want to find outliers from a set of documents and attach labels for each outlier group.

  • Random Forest Classifier Training (deprecated)

    Train a random forest classifier for text classification.

    This job is deprecated as of Fusion 5.2.0.
  • Word2Vec Model Training (Deprecated)

    Train a shallow neural model, and project each document onto this vector embedding space.

    This job is deprecated as of Fusion 5.2.0.

Data ingest

  • Parallel Bulk Loader

    The Parallel Bulk Loader (PBL) job enables bulk ingestion of structured and semi-structured data from big data systems, NoSQL databases, and common file formats like Parquet and Avro.

Legacy machine learning jobs

  • Legacy Item Recommender

    Compute user recommendations based on a pre-computed item similarity model.

  • Legacy Item Similarity

    Use this job when you only want to compute item-to-item similarities. This method is more lightweight than the generic Recommendations job.

    This job is deprecated as of Fusion AI 4.1.0. Use the ALS recommender job instead.