VQV Signal
A new pipeline for semantic user profiling shifts LLM inference from per-user to per-transaction-pattern, reducing costs at scale. It operates in three phases: resolving item names, inferring attributes for frequent patterns, and clustering attributes into queryable data.
A new pipeline for semantic user profiling shifts LLM inference from per-user to per-transaction-pattern, reducing costs at scale. It operates in three phases: resolving item names, inferring attributes for frequent patterns, and clustering attributes into queryable data.
AI-assisted summary based on the listed source.
Per-user LLM inference on transaction histories binds the inference budget linearly to user count, which becomes prohibitive at applied scale. We re-cast attribute inference from per-user to per-transaction-pattern. The pipeline runs in three phases: Resolve abstracts item names with optional web grounding,...
This approach addresses the prohibitive linear scaling of inference costs with user count in large-scale banking applications. By focusing on transaction patterns rather than individual users, it enables more efficient and scalable semantic profiling.
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