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Stop Treating Collisions Equally: Qualification-Aware Semantic ID Learning for Recommendation at Industrial Scale

简体中文

Fidelity: core mechanism. Paper online outcomes, local public-data results, and omissions are separate.

Paper information

Field Details
Paper arXiv 2603.00632
Organization University of Electronic Science and Technology of China / Kuaishou
First public release 2026-02-28 (arXiv v1)
Authors' code The paper did not provide official or author code when checked on 2026-08-24.
Adapter / method quasid
Local reproduction code src/auto_research/reproductions/quasid/

Original paper

Motivation and method

Use business qualifications to set collision-pair margins rather than repelling every pair equally. Items with sparse feedback or cold starts receive more distinguishable semantic IDs.

flowchart LR
 A["Residual-quantized SIDs"] --> B["Qualification-aware collisions"] --> C["Retrieval and ranking reuse"]

Key figure from the paper

Stop Treating Collisions Equally: Qualification-Aware Semantic ID Learning for Recommendation at Industrial Scale — Figure 1

Figure 1: Overview of the paper's method and key components. From the original paper; copyright remains with the authors.

Core equations

\[ L_{qual}=\sum_{ij}q_{ij}[m_{ij}-d(z_i,z_j)]_+ \]

Paper results

Online GMV-S2 +2.38% with 5% traffic for five days (Section 5.4). The private paper protocol is not directly comparable with local MovieLens measurements.

Local reproduction

Local comparison: The baseline is the shared transition + content scorer. The treatment adds only quasid on the same users, items, full-catalog candidates, and seed.

MovieLens-100K, 220 users and 360 items, seed 42: NDCG@10 0.0540 → 0.0537 (-0.55%); Hit@10 0.1091 → 0.0909. Validation selects mixture weights only; the test set is not used for tuning.

auto-research reproduce --paper quasid --dataset-dir data --seed 42

See the fixed metrics.

Reproduction boundaries

The core mechanism runs on MovieLens-100K with the same split, fixed full-catalog candidates, and seed. Private features, the production foundation model, and online traffic are unavailable. The implementation has independent model state and scoring and retains negative results. Local changes and paper A/B gains are reported separately.