Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation

• URL: http://arxiv.org/abs/2607.21028v2
• Date: Fri, 24 Jul 2026 09:38:27 GMT
• Title: Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation
• Abstract summary: Generative Recommendation (GR) has emerged as a new paradigm for sequential recommendation.<n>We propose BARGE, which employs Item Context-Aware Attention (ICA) to restore item-level structure during encoding.<n>Experiments and analytical studies on public benchmarks and a large-scale offline test demonstrate that BARGE achieves superior recommendation performance.
• Score: 10.616703360070359
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative Recommendation (GR) has emerged as a new paradigm for sequential recommendation, in which a representative line of work encodes items into hierarchical semantic IDs via residual quantization and predicts the IDs token by token. However, this generative formulation still exhibits structural gaps with respect to the recommendation task: flattening multi-token IDs into a single sequence destroys item-level structure, and the inconsistency between training and inference over a hierarchical codebook gives rise to semantic drift. To bridge these two gaps, we propose BARGE, which employs Item Context-Aware Attention (ICA) to restore item-level structure during encoding, and Hierarchical Path Reranking (HPR) together with Dual-Path Decoding (DPD) to suppress semantic drift from two complementary angles during decoding. Extensive experiments and analytical studies on public benchmarks and a large-scale offline test demonstrate that BARGE achieves superior recommendation performance. An online A/B test on a Tencent platform yields improvements of 0.60% in click-through rate, 1.34% in click unique visitors, and 1.70% in total reading time, confirming the practical value of BARGE in industrial-scale recommendation.

Related papers

• Tlow: Flow-based Item Tokenizer for Recommendation [44.2281760601041]
  Item tokenizer encodes semantic embeddings into token IDs to replace the randomly assigned item IDs.<n>Most common tokenizer, RQ-VAE, suffers from low decoding efficiency due to inherent dependencies among its codebooks.<n>We propose a funderlinelowbased item underlineTokenizer (Tlow) to transform raw semantic embeddings into a latent space.
  arXiv  Detail & Related papers  (2026-08-25T07:43:09Z)
• Beyond Fixed Depths and Widths: Optimizing Textual Decoding Tries in LLM-based Generative Recommendation [59.20704286413859]
  We propose a novel framework that co-designs textual term IDs and their underlying decoding trie.<n> BONSAI achieves up to a 21.6% relative improvement over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-07-18T04:31:02Z)
• Discrimination Is Generation: Unifying Ranking and Retrieval from a Tokenizer Perspective [14.332200648147863]
  We propose DIG (textbfDiscrimination textbfIs textbfGeneration), which embeds the tokenizer inside a discriminative ranking model for end-to-end training.<n> Experiments on three public benchmarks and two industrial datasets demonstrate that DIG simultaneously improves ranking, retrieval, and unified retrieval-ranking quality.
  arXiv  Detail & Related papers  (2026-05-14T13:59:29Z)
• Beyond Static Collision Handling: Adaptive Semantic ID Learning for Multimodal Recommendation at Industrial Scale [20.066503602593958]
  AdaSID regulates SID overlaps through a two-stage process.<n>It relaxes repulsion for observed overlaps when the involved items are semantically compatible.<n>It allocates the remaining regulation pressure according to local collision load and training progress.
  arXiv  Detail & Related papers  (2026-04-26T03:59:35Z)
• GenRec: A Preference-Oriented Generative Framework for Large-Scale Recommendation [14.663434490160016]
  We present GenRec, a preference-oriented generative framework deployed on the JD App.<n>In month-long online A/B tests, GenRec achieves 9.5% improvement in click count and 8.7% in transaction count over the existing pipeline.
  arXiv  Detail & Related papers  (2026-04-16T11:07:05Z)
• Masked Diffusion Generative Recommendation [14.679550929790151]
  Generative recommendation (GR) typically first quantizes continuous item embeddings into multi-level semantic IDs (SIDs)<n>We propose MDGR, a Masked Diffusion Generative Recommendation framework that reshapes the GR pipeline from three perspectives: codebook, training, and inference.
  arXiv  Detail & Related papers  (2026-01-27T11:39:02Z)
• TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Framework [62.66056331998838]
  TeaRAG is a token-efficient agentic RAG framework capable of compressing both retrieval content and reasoning steps.<n>Our reward function evaluates the knowledge sufficiency by a knowledge matching mechanism, while penalizing excessive reasoning steps.
  arXiv  Detail & Related papers  (2025-11-07T16:08:34Z)
• DiffGRM: Diffusion-based Generative Recommendation Model [63.35379395455103]
  Generative recommendation (GR) is an emerging paradigm that represents each item via a tokenizer as an n-digit semantic ID (SID)<n>We propose DiffGRM, a diffusion-based GR model that replaces the autoregressive decoder with a masked discrete diffusion model (MDM)<n> Experiments show consistent gains over strong generative and discriminative recommendation baselines on multiple datasets.
  arXiv  Detail & Related papers  (2025-10-21T03:23:32Z)
• Benchmarking Unified Face Attack Detection via Hierarchical Prompt Tuning [58.16354555208417]
  PAD and FFD are proposed to protect face data from physical media-based Presentation Attacks and digital editing-based DeepFakes, respectively.<n>The lack of a Unified Face Attack Detection model to simultaneously handle attacks in these two categories is mainly attributed to two factors.<n>We present a novel Visual-Language Model-based Hierarchical Prompt Tuning Framework that adaptively explores multiple classification criteria from different semantic spaces.
  arXiv  Detail & Related papers  (2025-05-19T16:35:45Z)
• Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations [22.48125906976824]
  We introduce the Cascaded Organized Bi-Represented generAtive retrieval framework, which integrates sparse semantic IDs and dense vectors through a cascading process.<n>Our method alternates between generating these representations by first generating sparse IDs, which serve as conditions to aid in the generation of dense vectors.<n>During inference, COBRA employs a coarse-to-fine strategy, starting with sparse ID generation and refining them into dense vectors via the generative model.
  arXiv  Detail & Related papers  (2025-03-04T10:00:05Z)
• Refine, Discriminate and Align: Stealing Encoders via Sample-Wise Prototypes and Multi-Relational Extraction [57.16121098944589]
  RDA is a pioneering approach designed to address two primary deficiencies prevalent in previous endeavors aiming at stealing pre-trained encoders. It is accomplished via a sample-wise prototype, which consolidates the target encoder's representations for a given sample's various perspectives. For more potent efficacy, we develop a multi-relational extraction loss that trains the surrogate encoder to Discriminate mismatched embedding-prototype pairs.
  arXiv  Detail & Related papers  (2023-12-01T15:03:29Z)
• Scalable Learning of Latent Language Structure With Logical Offline Cycle Consistency [71.42261918225773]
  Conceptually, LOCCO can be viewed as a form of self-learning where the semantic being trained is used to generate annotations for unlabeled text. As an added bonus, the annotations produced by LOCCO can be trivially repurposed to train a neural text generation model.
  arXiv  Detail & Related papers  (2023-05-31T16:47:20Z)

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.