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Related query

As explained in AI-Powered Search

A related query is a query whose meaning or search behavior is sufficiently associated with another query to form a useful query pair.

A related query is a query associated with another query through shared meaning or search behavior. Relatedness can include refinements, misspellings, or queries about the same topic, such as “iphone,” “pink phone case,” and “pink iphone case.” section 6.3

Query logs provide a practical signal: each record includes a user, query, and timestamp. Queries entered close together may represent a refinement or related topic, but reliable relationships require patterns repeated across many users rather than one person’s behavior. section 6.3

Worked query pairs

A query-log sample contains 725,459 keyword–user pairs and 244,876 co-occurring query pairs. Strong candidates include “green lantern” with “captain america” for 23 shared users, “iphone 4s” with “iphone” for 21, and “laptop” with “hp laptop” for 20. Sparse counts can still contain noise, so filtering and composite ranking remain necessary.

Figure 6.2 A typical sequence of searches from query logs for a particular user
figure

Inspect the search sequence to see how successive queries can reveal refinements, misspellings, and topical relationships. section 6.3

Related-query signals can support query expansion and improve recall, but they are not automatically reliable. General terms may co-occur frequently without a strong relationship, while limited co-occurrence data increases noise. section 6.3 section 6.3.1

How AI-Powered Search covers it

How deep it goes
Explains log transformation, co-occurrence counting, PMI2, composite ranking, and sparsity limitations without detailing later interaction signals.
Examples
Uses smartphone, electronics, entertainment, and computer query pairs, including “iphone 4s”–“iphone” and “laptop”–“hp laptop.”
What it assumes
Assumes query logs contain user identifiers, query text, timestamps, and signal types; teaches the pair transformation from that structure.
What you can do after
Apply it via Spark SQL to generate, filter, and rank related query pairs from user search logs.
How it connects
Connects related queries with query logs, user signals, query expansion, co-occurrence, and pointwise mutual information.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of AI-Powered Search. The section links point to the parts of the book it draws on.