Fusion

Version 5.3

Machine Learning Jobs

Fusion 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.

  • Synonym Detection Jobs

    Use this job to generate pairs of synonyms and pairs of similar queries. Two words are considered potential synonyms when they are used in a similar context in similar queries.

  • Token and Phrase Spell Correction

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

Signals aggregation

  • SQL Aggregation

    A Spark SQL aggregation job where user-defined parameters are injected into a built-in SQL template at runtime.

Experiment analysis

  • Ranking Metrics

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

Collaborative recommenders

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

query
count_i
type
timstamp_tdt
user_id
doc_id
session_id
fusion_query_id

Required signals fields:

required

required

required

required

required

Use the Query-to-Query Session-Based Similarity Jobs for better performance and query coverage.

Content-based recommenders

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

  • Content-Based Recommender

    Use this job when you want to compute item similarities based on their content, such as product descriptions.

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.)

  • Document Clustering

    The Document Clustering job uses an unsupervised machine learning algorithm to group documents into clusters based on similarities in their content. You can enable more efficient document exploration by using these clusters as facets, high-level summaries or themes, or to recommend other documents from the same cluster. The job can automatically group similar documents in all kinds of content, such as clinical trials, legal documents, book reviews, blogs, scientific papers, and products.

  • Logistic Regression Classifier Training

    Train a regularized logistic regression model for text classification.

The Classification job, introduced in Fusion 5.2.0, provides more options and better logging.
The Classification job, introduced in Fusion 5.2.0, provides more options and better logging.
  • Word2Vec Model Training (Deprecated)

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

Word2Vec Model Training 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.