The Three-Body Problem of Inverse Hybrid Search
Session Abstract
When users expect alerts for new products matching an uploaded image, the problem becomes inverse hybrid search. Unlike top-K search, alerting must guarantee fetch-all semantics: zero missed matches across all saved searches, combining vector similarity, boolean filters, and lexical signals. We show why this breaks traditional scaling intuition.
Session Description
Saved searches and alerts are common across e-commerce and marketplaces: price drops, availability notifications, and increasingly, visual alerts driven by images captured on mobile devices. While the user experience feels simple, the underlying system represents one of the most demanding forms of search.
This talk reframes alerting as a distinct retrieval discipline:
- Inverse: documents trigger queries, not the other way around
- Hybrid: vector similarity, boolean filters, and lexical constraints must all apply
- Fetch-All: every true match must be returned – no truncation, no approximation
We examine why traditional search assumptions fail under these constraints. In particular, we show how cost and instability are driven not by throughput (QPS), but by match cardinality – the number of alerts matched per incoming item – and how this interacts with scatter/gather execution, merge costs, and bursty ingestion patterns.
The talk focuses on:
- where inverse hybrid systems break silently
- why scaling infrastructure buys stability rather than throughput
- how correctness becomes an operational and economic concern
- why AI-driven recall often increases system pressure rather than reducing it
Attendees will leave with a concrete framework for reasoning about inverse hybrid search systems at scale.
