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PushDualGen: Enabling LLMs to Generate Semantic IDs with Interpretable Copy for Industrial Push Recommendation

简体中文

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

Paper information

Field Details
Paper arXiv 2608.07989
Organization Kuaishou Technology
First public release 2026-08-08 (arXiv v1)
Authors' code The paper did not provide official or author code when checked on 2026-08-24.
Adapter / method pushdualgen
Local reproduction code src/auto_research/reproductions/pushdualgen/

Original paper

Motivation and method

Generate a servable semantic ID first, then optional explanatory copy only when needed. Serving fuses SID and copy representations while preserving explanation consistency.

flowchart LR
 A["SID compression"] --> B["SID to optional copy"] --> C["Representation fusion"]

Key figure from the paper

PushDualGen: Enabling LLMs to Generate Semantic IDs with Interpretable Copy for Industrial Push Recommendation — Figure 1

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

Core equations

\[ p(s,c|h)=p(s|h)\,p(c|s,h)^{\mathbf1[g(h,s)=1]} \]

Paper results

The main online result is effective play rate +8.50% over 14 days at 15% traffic (150M users). Offline and online metrics use private or paper-specific protocols. 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 pushdualgen on the same users, items, full-catalog candidates, and seed.

MovieLens-100K, 220 users and 360 items, seed 42: NDCG@10 0.0540 → 0.0546 (+1.19%); Hit@10 0.1091 → 0.0955. Validation selects mixture weights only; the test set is not used for tuning.

auto-research reproduce --paper pushdualgen --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, rather than mapping a paper name to a shared weighted formula. See the adapter's omitted_core_components for omissions. Local changes and paper A/B gains are reported separately.