Machine Learning Jobs

Table of Contents

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.

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.

SQL Aggregation

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

Ranking Metrics

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

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

BPR Recommender

Use this job when you want to compute user recommendations or item similarities using a Bayesian Personalized Ranking (BPR) recommender algorithm.
Query-to-Query Session-Based Similarity jobs

This recommender is based on co-occurrence of queries in the context of clicked documents and sessions. It is useful when your data shows that users tend to search for similar items in a single search session. This method of generating query-to-query recommendations is faster and more reliable than the Query-to-Query Similarity recommender job, and is session-based unlike the similar queries previously generated as part of the Synonym Detection job.

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.

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.
Classification job

This job analyzes how your existing documents are categorized and produces a classification model that can be used to predict the categories of new documents at index time.
Outlier Detection

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

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.