Journal of Technology Informatics and Engineering
Vol. 5 No. 1 (2026): APRIL | JTIE : Journal of Technology Informatics and Engineering

IntentRouter-QAC: Distilling LLM-Derived Intent Signals into Small Language Models for Context-Aware Autosuggest and Search Entry Routing

Haowei Tu (Information Systems, New York University, NY, USA)
Yinchen Shi (Computer Science, New York University, NY, USA)
Sophia Chen (Computer Science, University of Illinois Urbana-Champaign, Urbana, IL, USA)



Article Info

Publish Date
30 Apr 2026

Abstract

Search boxes increasingly function as routing interfaces: a partial query may require a completion, immediate submission, a context-sensitive suggestion, or a fallback action. This paper evaluates IntentRouter-QAC as a lightweight lexical routing framework and tests product retrieval independently. Five mutually exclusive operational outcomes are derived within the 20,000-row AmazonQAC test file and evaluated with a strict session-disjoint temporal split; product-query provenance is not used as a route class. Across five seeds, the combined word/character TF-IDF router reached 0.473 mean accuracy (SD 0.031) and 0.330 mean macro-F1 (SD 0.018), indicating uneven performance across routes. On the 951-row temporal test set, context-nearest completion achieved 0.033 Success@10 and 0.025 MRR@10, compared with 0.029 and 0.023 for confidence-gated routing. The paired MRR@10 difference was 0.002 (95% CI -0.007 to 0.010; Holm-adjusted p = 1.000). Product retrieval on 480 WANDS queries and 42,994 products produced a different pattern: a word/character lexical hybrid reached 0.690 nDCG@10 and 0.504 Exact-MRR@10, significantly exceeding the title-only baseline on both measures. Route-aware decision-making is therefore useful as an architectural separation of entry actions, but it does not automatically improve exact autocomplete ranking over a strong session-context baseline.

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Journal Info

Abbrev

jtie

Publisher

Subject

Computer Science & IT

Description

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