IntRR: A Framework for Integrating SID Redistribution and Length Reduction

• URL: http://arxiv.org/abs/2602.20704v1
• Date: Tue, 24 Feb 2026 09:09:40 GMT
• Title: IntRR: A Framework for Integrating SID Redistribution and Length Reduction
• Abstract summary: We propose IntRR, a novel framework that integrates objective-aligned SID Redistribution and structural Length Reduction.<n>IntRR yields substantial improvements over representative generative baselines, achieving superior performance in both recommendation accuracy and efficiency.
• Score: 14.327886721362647
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative Recommendation (GR) has emerged as a transformative paradigm that reformulates the traditional cascade ranking system into a sequence-to-item generation task, facilitated by the use of discrete Semantic IDs (SIDs). However, current SIDs are suboptimal as the indexing objectives (Stage 1) are misaligned with the actual recommendation goals (Stage 2). Since these identifiers remain static (Stage 2), the backbone model lacks the flexibility to adapt them to the evolving complexities of user interactions. Furthermore, the prevailing strategy of flattening hierarchical SIDs into token sequences leads to sequence length inflation, resulting in prohibitive computational overhead and inference latency. To address these challenges, we propose IntRR, a novel framework that integrates objective-aligned SID Redistribution and structural Length Reduction. By leveraging item-specific Unique IDs (UIDs) as collaborative anchors, this approach dynamically redistributes semantic weights across hierarchical codebook layers. Concurrently, IntRR handles the SID hierarchy recursively, eliminating the need to flatten sequences. This ensures a fixed cost of one token per item. Extensive experiments on benchmark datasets demonstrate that IntRR yields substantial improvements over representative generative baselines, achieving superior performance in both recommendation accuracy and efficiency.

Related papers

• Accelerating Unified Multimodal Models with Core-Expansion Routing and Unified Computation Scheduling [61.28599393707943]
  Unified multimodal models jointly support understanding and generation, but incur substantial redundant computation across tokens, layers, and generation timesteps.<n>We propose a task-shared core scorer and progress-conditioned generation expansions, optimized through generation decomposition and cross-task core alignment.<n> Experiments on two representative UMM architectures demonstrate consistent quality--efficiency improvements across both tasks, retaining 98.03% of dense understanding performance with a 1.93times$ end-to-end inference speedup.
  arXiv  Detail & Related papers  (2026-08-29T14:27:55Z)
• CogRec: Structure-Cognitive Fast-and-Slow Reasoning for Generative Recommendation [29.663400191042996]
  CogRec is a structure-cognitive fast-and-slow reasoning framework.<n>CogRec augments the vertical SID hierarchy with intra-layer semantic graphs and item-level neighborhoods.<n> SID Routing represents recommendation reasoning through layer-wise Match, LateralJump, and Explore operations.
  arXiv  Detail & Related papers  (2026-07-27T13:19:55Z)
• Empowering Cross-Domain Sequential Recommendation with Hybrid Tokenization and Serial-Parallel Decoding [63.74998371740272]
  Cross-domain sequential recommendation (CDSR) aims to model users' dynamic interest transitions and sequential patterns across multiple domains.<n>We propose GenCDSR, an effective and efficient generative framework for CDSR.<n>We show that GenCDSR achieves an average accuracy improvement of 1.5 percent and an average inference latency reduction of 85.1 percent compared with state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-07-21T08:01:15Z)
• Conditional Memory Enhanced Item Representation for Generative Recommendation [46.59623438612085]
  Generative recommendation (GR) has emerged as a promising paradigm that predicts target items by autoregressively generating their semantic identifiers (SID)<n>Most GR methods follow a quantization-representation-generation pipeline, first assigning each item a SID, then constructing input representations from SID-token embeddings, and finally predicting the target SID through autoregressive generation.<n>We propose ComeIR, a Conditional Memory enhanced Item Representation framework that reconstructs SID-token embeddings into item-aware inputs and restores the token granularity during SID decoding.
  arXiv  Detail & Related papers  (2026-05-12T02:56:59Z)
• Reasoning over Semantic IDs Enhances Generative Recommendation [56.83043583765322]
  We propose a two-stage framework that elicits reasoning over SIDs by strengthening SID--language alignment.<n> SIDReasoner further improves recommendation reasoning through outcome-driven reinforced optimization.<n>Our experiments on three real-world datasets demonstrate the effectiveness of our reasoning-augmented SID-based generative recommendation.
  arXiv  Detail & Related papers  (2026-03-24T13:31:48Z)
• End-to-End Semantic ID Generation for Generative Advertisement Recommendation [33.453121305193434]
  We propose a Unified SID generation framework for generative advertisement recommendation.<n>Specifically, we jointly optimize embeddings and SIDs in an end-to-end manner from raw advertising data.<n>Experiments demonstrate that UniSID consistently outperforms state-of-the-art SID generation methods.
  arXiv  Detail & Related papers  (2026-02-11T02:38:26Z)
• R2LED: Equipping Retrieval and Refinement in Lifelong User Modeling with Semantic IDs for CTR Prediction [23.668401664583758]
  We propose a novel paradigm that equips retrieval and refinement in Lifelong User Modeling with SEmantic IDs (R2LED)<n>First, we introduce a Multi-route Mixed Retrieval for the retrieval stage. On the other hand, a mixed retrieval mechanism is proposed to efficiently retrieve candidates from both collaborative and semantic views.<n>For refinement, we design a Bi-level Fusion Refinement, including a target-aware cross-attention for route-level fusion and a gate mechanism for SID-level fusion.
  arXiv  Detail & Related papers  (2026-02-06T11:27:20Z)
• GLASS: A Generative Recommender for Long-sequence Modeling via SID-Tier and Semantic Search [51.44490997013772]
  GLASS is a novel framework that integrates long-term user interests into the generative process via SID-Tier and Semantic Search.<n>We show that GLASS outperforms state-of-the-art baselines in experiments on two large-scale real-world datasets.
  arXiv  Detail & Related papers  (2026-02-05T13:48:33Z)
• Rethinking Generative Recommender Tokenizer: Recsys-Native Encoding and Semantic Quantization Beyond LLMs [17.944727019161878]
  ReSID is a principled, SID framework that recommend learning from the perspective of information preservation and sequential predictability.<n>It consistently outperforms strong sequential and SID-based generative baselines by an average of over 10%, while reducing tokenization cost by up to 122x.
  arXiv  Detail & Related papers  (2026-02-02T17:00:04Z)
• Differentiable Semantic ID for Generative Recommendation [65.83703273297492]
  Generative recommendation provides a novel paradigm in which each item is represented by a discrete semantic ID (SID) learned from rich content.<n>In practice, SIDs are typically optimized only for content reconstruction rather than recommendation accuracy.<n>A natural approach is to make semantic indexing differentiable so that recommendation gradients can directly influence SID learning.<n>We propose DIGER, a first step toward effective differentiable semantic IDs for generative recommendation.
  arXiv  Detail & Related papers  (2026-01-27T15:34:11Z)
• PRISM: Purified Representation and Integrated Semantic Modeling for Generative Sequential Recommendation [28.629759086187352]
  We propose a novel generative recommendation framework, PRISM, with Purified Representation and Integrated Semantic Modeling.<n>PRISM consistently outperforms state-of-the-art baselines across four real-world datasets.
  arXiv  Detail & Related papers  (2026-01-23T08:50:16Z)
• A Simple and Effective Framework for Symmetric Consistent Indexing in Large-Scale Dense Retrieval [11.72564658353791]
  Dense retrieval has become the industry standard in large-scale information retrieval systems due to its high efficiency and competitive accuracy.<n>The widely adopted dual-tower encoding architecture introduces inherent challenges, primarily representational space misalignment and retrieval index inconsistency.<n>This paper proposes a simple and effective framework named SCI comprising two synergistic modules.<n>We provide theoretical guarantees for our approach, with its effectiveness validated by results across public datasets and real-world e-commerce datasets.
  arXiv  Detail & Related papers  (2025-12-15T08:11:24Z)
• The Best of the Two Worlds: Harmonizing Semantic and Hash IDs for Sequential Recommendation [51.62815306481903]
  We propose textbfname, a novel framework that harmonizes the SID and HID. Specifically, we devise a dual-branch modeling architecture that enables the model to capture both the multi-granular semantics within SID while preserving the unique collaborative identity of HID.<n>Experiments on three real-world datasets show that name balances recommendation quality for both head and tail items while surpassing the existing baselines.
  arXiv  Detail & Related papers  (2025-12-11T07:50:53Z)
• ReCAP: Recursive Context-Aware Reasoning and Planning for Large Language Model Agents [61.51091799997476]
  We introduce ReCAP (Recursive Context-Aware Reasoning and Planning), a hierarchical framework with shared context for reasoning and planning in large language models (LLMs)<n>ReCAP combines three key mechanisms: plan-ahead decomposition, structured re-injection of parent plans, and memory-efficient execution.<n>Experiments demonstrate that ReCAP substantially improves subgoal alignment and success rates on various long-horizon reasoning benchmarks.
  arXiv  Detail & Related papers  (2025-10-27T20:03:55Z)
• Understanding Generative Recommendation with Semantic IDs from a Model-scaling View [57.471604518714535]
  Generative Recommendation (GR) tries to unify rich item semantics and collaborative filtering signals.<n>One popular modern approach is to use semantic IDs (SIDs) to represent items in an autoregressive user interaction sequence modeling setup.<n>We show that SID-based GR shows significant bottlenecks while scaling up the model.<n>We revisit another GR paradigm that directly uses large language models (LLMs) as recommenders.
  arXiv  Detail & Related papers  (2025-09-29T21:24:17Z)
• Dual-Refinement: Joint Label and Feature Refinement for Unsupervised Domain Adaptive Person Re-Identification [51.98150752331922]
  Unsupervised domain adaptive (UDA) person re-identification (re-ID) is a challenging task due to the missing of labels for the target domain data. We propose a novel approach, called Dual-Refinement, that jointly refines pseudo labels at the off-line clustering phase and features at the on-line training phase. Our method outperforms the state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2020-12-26T07:35:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.