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Session

As explained in AI-Powered Search

A session is a bounded sequence of interactions associated with a user or client context during a period of activity.

A session is a bounded sequence of interactions associated with a user or client context during a period of activity. In search, it groups requests closely enough to interpret their shared context and behavior. section 4.1.4

Session identity sits between longer-lived identifiers and individual requests. A user ID may persist across devices; device and browser IDs persist across broader scopes; a session ID persists only through one session; a request ID lasts for one request. Cookies can link requests to sessions and sessions to browser identities. section 4.1.4

Search-session records commonly retain:

  • A session identifier, query, ranked results, and a click indicator for each result. section 11.1.3
  • The presented ranking, which may differ between sessions because the index or relevance algorithm changed. section 11.1.3
  • Enough repeated session data to calculate result-level click-through rates and derive judgments for learning to rank. section 11.1.3

A search session as a data row group

For the query "transformers dark of the moon," session 3 records results at ranks 0 through 14 and whether each result was clicked. Grouping many such sessions by document produces click counts, session counts, and click-through rates. section 11.1.3

Listing 11.3 Examining a search session
query = "transformers dark of the moon"
sessions = get_sessions(query)  #1
print(sessions.loc[3]) #2

Inspect a session by querying the data for a search term and selecting one session identifier. section 11.1.3

How AI-Powered Search covers it

How deep it goes
Defines identifier scope and shows session-level click data, but stops short of specifying a universal session-timeout rule.
Examples
Uses a Transformers movie query, session 3, ranked documents, click flags, pandas inspection, and CTR aggregation.
What it assumes
Assumes search requests and user signals, while introducing identifier durability and session grouping progressively.
What you can do after
Apply session records via pandas to inspect interactions and prepare click-based judgments for learning to rank.
How it connects
Connects sessions with users, device IDs, browser IDs, request IDs, cookies, click models, click-through rate, and learning to rank.

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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.