BBQRec: Behavior-Bind Quantization for Multi-Modal Sequential Recommendation

• URL: http://arxiv.org/abs/2504.06636v1
• Date: Wed, 09 Apr 2025 07:19:48 GMT
• Title: BBQRec: Behavior-Bind Quantization for Multi-Modal Sequential Recommendation
• Authors: Kaiyuan Li, Rui Xiang, Yong Bai, Yongxiang Tang, Yanhua Cheng, Xialong Liu, Peng Jiang, Kun Gai,
• Abstract summary: We propose a Behavior-Bind multi-modal Quantization for Sequential Recommendation (BBQRec) featuring dual-aligned quantization and semantics-aware sequence modeling.<n>BBQRec disentangles modality-agnostic behavioral patterns from noisy modality-specific features through contrastive codebook learning.<n>We design a discretized similarity reweighting mechanism that dynamically adjusts self-attention scores using quantized semantic relationships.
• Score: 15.818669767036592
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-modal sequential recommendation systems leverage auxiliary signals (e.g., text, images) to alleviate data sparsity in user-item interactions. While recent methods exploit large language models to encode modalities into discrete semantic IDs for autoregressive prediction, we identify two critical limitations: (1) Existing approaches adopt fragmented quantization, where modalities are independently mapped to semantic spaces misaligned with behavioral objectives, and (2) Over-reliance on semantic IDs disrupts inter-modal semantic coherence, thereby weakening the expressive power of multi-modal representations for modeling diverse user preferences. To address these challenges, we propose a Behavior-Bind multi-modal Quantization for Sequential Recommendation (BBQRec for short) featuring dual-aligned quantization and semantics-aware sequence modeling. First, our behavior-semantic alignment module disentangles modality-agnostic behavioral patterns from noisy modality-specific features through contrastive codebook learning, ensuring semantic IDs are inherently tied to recommendation tasks. Second, we design a discretized similarity reweighting mechanism that dynamically adjusts self-attention scores using quantized semantic relationships, preserving multi-modal synergies while avoiding invasive modifications to the sequence modeling architecture. Extensive evaluations across four real-world benchmarks demonstrate BBQRec's superiority over the state-of-the-art baselines.

Related papers

• UniT: Unified Multimodal Chain-of-Thought Test-time Scaling [85.590774707406]
  Unified models can handle both multimodal understanding and generation within a single architecture, yet they typically operate in a single pass without iteratively refining their outputs.<n>We introduce UniT, a framework for multimodal test-time scaling that enables a single unified model to reason, verify, and refine across multiple rounds.
  arXiv  Detail & Related papers  (2026-02-12T18:59:49Z)
• 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)
• Q-BERT4Rec: Quantized Semantic-ID Representation Learning for Multimodal Recommendation [5.699357781063521]
  We propose Q-Bert4Rec, a sequential recommendation framework that unifies semantic representation and quantized modeling.<n>We validate our model on public Amazon benchmarks and demonstrate that Q-Bert4Rec significantly outperforms many strong existing methods.<n>Our source code will be publicly available on GitHub after publishing.
  arXiv  Detail & Related papers  (2025-12-02T07:06:44Z)
• LLaDA-Rec: Discrete Diffusion for Parallel Semantic ID Generation in Generative Recommendation [32.284624021041004]
  We propose LLaDA-Rec, a discrete diffusion framework that reformulates recommendation as parallel semantic ID generation.<n> Experiments on three real-world datasets show that LLaDA-Rec consistently outperforms both ID-based and state-of-the-art generative recommenders.
  arXiv  Detail & Related papers  (2025-11-09T07:12:15Z)
• Merge and Guide: Unifying Model Merging and Guided Decoding for Controllable Multi-Objective Generation [49.98025799046136]
  We introduce Merge-And-GuidE, a two-stage framework that leverages model merging for guided decoding.<n>In Stage 1, MAGE resolves a compatibility problem between the guidance and base models.<n>In Stage 2, we merge explicit and implicit value models into a unified guidance proxy, which then steers the decoding of the base model from Stage 1.
  arXiv  Detail & Related papers  (2025-10-04T11:10:07Z)
• Progressive Semantic Residual Quantization for Multimodal-Joint Interest Modeling in Music Recommendation [6.790539226766362]
  We propose a novel multimodal recommendation framework with two stages.<n>In the first stage, our method generates modal-specific and modal-joint semantic IDs.<n>In the second stage, to model multimodal interest of users, a Multi-Codebook Cross-Attention network is designed.
  arXiv  Detail & Related papers  (2025-08-28T02:16:57Z)
• Semantic Item Graph Enhancement for Multimodal Recommendation [49.66272783945571]
  Multimodal recommendation systems have attracted increasing attention for their improved performance by leveraging items' multimodal information.<n>Prior methods often build modality-specific item-item semantic graphs from raw modality features.<n>These semantic graphs suffer from semantic deficiencies, including insufficient modeling of collaborative signals among items.
  arXiv  Detail & Related papers  (2025-08-08T09:20:50Z)
• FindRec: Stein-Guided Entropic Flow for Multi-Modal Sequential Recommendation [50.438552588818]
  We propose textbfFindRec (textbfFlexible unified textbfinformation textbfdisentanglement for multi-modal sequential textbfRecommendation)<n>A Stein kernel-based Integrated Information Coordination Module (IICM) theoretically guarantees distribution consistency between multimodal features and ID streams.<n>A cross-modal expert routing mechanism that adaptively filters and combines multimodal features based on their contextual relevance.
  arXiv  Detail & Related papers  (2025-07-07T04:09:45Z)
• MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings [75.0617088717528]
  MoCa is a framework for transforming pre-trained VLM backbones into effective bidirectional embedding models.<n>MoCa consistently improves performance across MMEB and ViDoRe-v2 benchmarks, achieving new state-of-the-art results.
  arXiv  Detail & Related papers  (2025-06-29T06:41:00Z)
• SpecRouter: Adaptive Routing for Multi-Level Speculative Decoding in Large Language Models [21.933379266533098]
  Large Language Models (LLMs) present a critical trade-off between inference quality and computational cost.<n>Existing serving strategies often employ fixed model scales or static two-stage speculative decoding.<n>This paper introduces systemname, a novel framework that reimagines LLM inference as an adaptive routing problem.
  arXiv  Detail & Related papers  (2025-05-12T15:46:28Z)
• Teaching Metric Distance to Autoregressive Multimodal Foundational Models [21.894600900013316]
  We introduce DIST2Loss, a distance-aware framework designed to train autoregressive discrete models.<n>DIST2Loss transforms exponential family distributions derived from inherent distance metrics into discrete, categorical optimization targets.<n> Empirical evaluations show consistent performance gains in diverse multimodal applications.
  arXiv  Detail & Related papers  (2025-03-04T08:14:51Z)
• Enhancing Unimodal Latent Representations in Multimodal VAEs through Iterative Amortized Inference [20.761803725098005]
  Multimodal variational autoencoders (VAEs) aim to capture shared latent representations by integrating information from different data modalities. A significant challenge is accurately inferring representations from any subset of modalities without training an impractical number of inference networks for all possible modality combinations. We introduce multimodal iterative amortized inference, an iterative refinement mechanism within the multimodal VAE framework.
  arXiv  Detail & Related papers  (2024-10-15T08:49:38Z)
• Unleash LLMs Potential for Recommendation by Coordinating Twin-Tower Dynamic Semantic Token Generator [60.07198935747619]
  We propose Twin-Tower Dynamic Semantic Recommender (T TDS), the first generative RS which adopts dynamic semantic index paradigm. To be more specific, we for the first time contrive a dynamic knowledge fusion framework which integrates a twin-tower semantic token generator into the LLM-based recommender. The proposed T TDS recommender achieves an average improvement of 19.41% in Hit-Rate and 20.84% in NDCG metric, compared with the leading baseline methods.
  arXiv  Detail & Related papers  (2024-09-14T01:45:04Z)
• Disentangling ID and Modality Effects for Session-based Recommendation [46.09367252640389]
  We propose a novel framework DIMO to disentangle the effects of ID and modality in the task. DIMO provides recommendations via causal inference and further creates two templates for generating explanations.
  arXiv  Detail & Related papers  (2024-04-19T15:54:46Z)
• Enhancing Multimodal Unified Representations for Cross Modal Generalization [52.16653133604068]
  We propose Training-free Optimization of Codebook (TOC) and Fine and Coarse cross-modal Information Disentangling (FCID)<n>These methods refine the unified discrete representations from pretraining and perform fine- and coarse-grained information disentanglement tailored to the specific characteristics of each modality.
  arXiv  Detail & Related papers  (2024-03-08T09:16:47Z)
• Minimally-Supervised Speech Synthesis with Conditional Diffusion Model and Language Model: A Comparative Study of Semantic Coding [57.42429912884543]
  We propose Diff-LM-Speech, Tetra-Diff-Speech and Tri-Diff-Speech to solve high dimensionality and waveform distortion problems. We also introduce a prompt encoder structure based on a variational autoencoder and a prosody bottleneck to improve prompt representation ability. Experimental results show that our proposed methods outperform baseline methods.
  arXiv  Detail & Related papers  (2023-07-28T11:20:23Z)
• Object Segmentation by Mining Cross-Modal Semantics [68.88086621181628]
  We propose a novel approach by mining the Cross-Modal Semantics to guide the fusion and decoding of multimodal features. Specifically, we propose a novel network, termed XMSNet, consisting of (1) all-round attentive fusion (AF), (2) coarse-to-fine decoder (CFD), and (3) cross-layer self-supervision.
  arXiv  Detail & Related papers  (2023-05-17T14:30:11Z)
• Denoising-Contrastive Alignment for Continuous Sign Language Recognition [22.800767994061175]
  Continuous sign language recognition aims to recognize signs in untrimmed sign language videos to textual glosses.<n>Current cross-modality alignment paradigms often neglect the role of textual grammar to guide the video representation.<n>We propose a Denoising-Contrastive Alignment paradigm to enhance video representations.
  arXiv  Detail & Related papers  (2023-05-05T15:20:27Z)
• Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning [53.68371566336254]
  We argue that the key to better performance lies in meaningful latent modality structures instead of perfect modality alignment. Specifically, we design 1) a deep feature separation loss for intra-modality regularization; 2) a Brownian-bridge loss for inter-modality regularization; and 3) a geometric consistency loss for both intra- and inter-modality regularization.
  arXiv  Detail & Related papers  (2023-03-10T14:38:49Z)
• Mutual Exclusivity Training and Primitive Augmentation to Induce Compositionality [84.94877848357896]
  Recent datasets expose the lack of the systematic generalization ability in standard sequence-to-sequence models. We analyze this behavior of seq2seq models and identify two contributing factors: a lack of mutual exclusivity bias and the tendency to memorize whole examples. We show substantial empirical improvements using standard sequence-to-sequence models on two widely-used compositionality datasets.
  arXiv  Detail & Related papers  (2022-11-28T17:36:41Z)
• Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization [76.68866368409216]
  We propose learning to dynamically select discretization tightness conditioned on inputs. We show that dynamically varying tightness in communication bottlenecks can improve model performance on visual reasoning and reinforcement learning tasks.
  arXiv  Detail & Related papers  (2022-02-02T23:54:26Z)
• Improve Variational Autoencoder for Text Generationwith Discrete Latent Bottleneck [52.08901549360262]
  Variational autoencoders (VAEs) are essential tools in end-to-end representation learning. VAEs tend to ignore latent variables with a strong auto-regressive decoder. We propose a principled approach to enforce an implicit latent feature matching in a more compact latent space.
  arXiv  Detail & Related papers  (2020-04-22T14:41:37Z)

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.