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arxiv: 2605.09040 · v2 · submitted 2026-05-09 · 💻 cs.AI · cs.IR· cs.LG

Recognition: no theorem link

UxSID: Semantic-Aware User Interests Modeling for Ultra-Long Sequence

Hongwei Zhang , Qiqiang Zhong , Jiangxia Cao , Yiyang Lv , Huanjie Wang , Liwei Guan , Jing Yao , Yiyu Wang
show 3 more authors
Junfeng Shu Zhaojie Liu Han Li
Authors on Pith no claims yet

Pith reviewed 2026-05-14 21:01 UTC · model grok-4.3

classification 💻 cs.AI cs.IRcs.LG
keywords semantic IDsultra-long sequencesdual-level attentionuser interest modelingrecommendation systemsadvertisingsequence compression
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The pith

UxSID uses semantic IDs and dual-level attention to model ultra-long user sequences with target-aware preferences.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces UxSID to solve the efficiency-effectiveness trade-off when handling very long user behavior histories in recommendation and advertising systems. Existing approaches either scan every past item individually at high cost or compress the entire history without regard to the current target item. UxSID instead assigns Semantic IDs to group related items and applies a two-stage attention process that first pools within each semantic group and then attends across groups to the target. This produces a shared interest memory that stays semantically aware yet remains computationally light. The method reaches state-of-the-art accuracy and delivers a measured 0.337 percent revenue gain in a large-scale live advertising experiment.

Core claim

By assigning Semantic IDs to items and employing a dual-level attention strategy over the resulting semantic groups, UxSID builds a shared interest memory that captures preferences relevant to a specific target item without incurring the cost of item-by-item search or the information loss of fully item-agnostic compression.

What carries the argument

Semantic IDs (SIDs) that group items by meaning, combined with dual-level attention that first aggregates within each semantic group and then attends across groups to the target item.

Load-bearing premise

Grouping items into Semantic IDs preserves the distinctions that actually matter for target-aware user preferences rather than collapsing important differences or injecting new biases.

What would settle it

Replace the learned Semantic IDs with randomly assigned group labels on the same ultra-long sequences and measure whether recommendation accuracy and revenue lift disappear.

Figures

Figures reproduced from arXiv: 2605.09040 by Han Li, Hongwei Zhang, Huanjie Wang, Jiangxia Cao, Jing Yao, Junfeng Shu, Liwei Guan, Qiqiang Zhong, Yiyang Lv, Yiyu Wang, Zhaojie Liu.

Figure 1
Figure 1. Figure 1: Comparison of different paradigms for ULSM. (a) Item-specific Search: Online filtering for each candidate, incurring high computational cost. (b) Item-agnostic Compression: Offline distillation into static memories, lacking target-specificity. (c) UxSID: A semantic-specific path that shares compressed interest memories among items with the same SIDs. platform’s items, a powerful recommendation system (RecS… view at source ↗
Figure 2
Figure 2. Figure 2: The architecture of UxSID primarily comprises three components: a target SIDs Generator [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: AUC improvements (percentage points) across various sequence lengths on all datasets. [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Hyper-parameters analysis of UxSID on all three datasets. [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Efficacy of UxSID in interest modeling. (a) highlights the target SID-based attention [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The overall system deployment pipeline of UxSID, comprising offline UxSID embedding [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
read the original abstract

Modeling ultra-long user sequences involves a difficult trade-off between efficiency and effectiveness. While current paradigms rely on either item-specific search or item-agnostic compression, we propose UxSID, a framework exploring a third path: semantic-group shared interest memory. By utilizing Semantic IDs (SIDs) and a dual-level attention strategy, UxSID captures target-aware preferences without the heavy cost of item-specific models. This end-to-end architecture balances computational parsimony with semantic awareness, achieving state-of-the-art performance and a 0.337% revenue lift in large-scale advertising A/B test.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The paper proposes UxSID, a framework for modeling ultra-long user sequences in recommendation systems via semantic-group shared interest memory. It introduces Semantic IDs (SIDs) and a dual-level attention strategy to capture target-aware preferences efficiently, avoiding the costs of item-specific search while retaining semantic awareness, and reports state-of-the-art performance plus a 0.337% revenue lift in a large-scale advertising A/B test.

Significance. If the results hold, the work offers a practical third path for ultra-long sequence modeling that could improve scalability in industrial recommender systems without sacrificing semantic fidelity. The online A/B test result provides direct evidence of business impact, strengthening the case for adoption in advertising and related domains.

minor comments (2)
  1. [Abstract] Abstract: the claim of SOTA performance would be strengthened by briefly naming the offline datasets, sequence lengths, and main baselines used.
  2. [§4] §4 (Experiments): include error bars or statistical significance tests for the reported metrics to support the SOTA and revenue-lift claims.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive assessment of UxSID and the recommendation for minor revision. The recognition of our semantic-group shared interest memory approach as a practical third path for ultra-long sequence modeling, along with the value of the online A/B test results, is appreciated.

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper proposes UxSID as an independent architectural framework using Semantic IDs (SIDs) and dual-level attention to model ultra-long sequences via semantic-group shared memory. No equations, fitted parameters, or derivations are presented in the abstract or described structure that reduce outputs to inputs by construction. The central claim of balancing efficiency and target-aware preferences is framed as a novel third path without self-definitional loops, self-citation load-bearing premises, or renaming of known results. The architecture is presented as self-contained with external validation via A/B test revenue lift, satisfying the criteria for a non-circular proposal.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 2 invented entities

The abstract introduces Semantic IDs and dual-level attention as core components without detailing their construction or grounding; these function as new modeling primitives whose validity is assumed rather than derived from external benchmarks.

invented entities (2)
  • Semantic IDs (SIDs) no independent evidence
    purpose: To group items into semantic clusters for shared interest memory
    Introduced as the basis for semantic-group modeling; no independent evidence or prior definition supplied in abstract
  • dual-level attention strategy no independent evidence
    purpose: To capture target-aware preferences at group and cross-group levels
    Proposed as the mechanism balancing efficiency and semantic awareness; no formal definition or validation in available text

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Reference graph

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