Beyond Unimodal Boundaries: Generative Recommendation with Multimodal Semantics

• URL: http://arxiv.org/abs/2503.23333v1
• Date: Sun, 30 Mar 2025 06:24:43 GMT
• Title: Beyond Unimodal Boundaries: Generative Recommendation with Multimodal Semantics
• Authors: Jing Zhu, Mingxuan Ju, Yozen Liu, Danai Koutra, Neil Shah, Tong Zhao,
• Abstract summary: We argue that this is a significant limitation given the rich, multimodal nature of real-world data.<n>We reveal that GR models are particularly sensitive to different modalities and examine the challenges in achieving effective GR.<n>We introduce MGR-LF++, an enhanced late fusion framework that employs contrastive modality alignment and special tokens to denote different modalities.
• Score: 46.79459036259515
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative recommendation (GR) has become a powerful paradigm in recommendation systems that implicitly links modality and semantics to item representation, in contrast to previous methods that relied on non-semantic item identifiers in autoregressive models. However, previous research has predominantly treated modalities in isolation, typically assuming item content is unimodal (usually text). We argue that this is a significant limitation given the rich, multimodal nature of real-world data and the potential sensitivity of GR models to modality choices and usage. Our work aims to explore the critical problem of Multimodal Generative Recommendation (MGR), highlighting the importance of modality choices in GR nframeworks. We reveal that GR models are particularly sensitive to different modalities and examine the challenges in achieving effective GR when multiple modalities are available. By evaluating design strategies for effectively leveraging multiple modalities, we identify key challenges and introduce MGR-LF++, an enhanced late fusion framework that employs contrastive modality alignment and special tokens to denote different modalities, achieving a performance improvement of over 20% compared to single-modality alternatives.

Related papers

• Beyond Unimodal Shortcuts: MLLMs as Cross-Modal Reasoners for Grounded Named Entity Recognition [51.68340973140949]
  Multimodal Named Entity Recognition (GMNER) aims to extract text-based entities, assign them semantic categories, and ground them to corresponding visual regions.<n> MLLMs exhibit $textbfmodality bias$, including visual bias and textual bias, which stems from their tendency to take unimodal shortcuts.<n>We propose Modality-aware Consistency Reasoning ($bfMCR$), which enforces structured cross-modal reasoning.
  arXiv  Detail & Related papers  (2026-02-04T12:12:49Z)
• Toward Effective Multimodal Graph Foundation Model: A Divide-and-Conquer Based Approach [42.970648490410504]
  Multimodal Graph Foundation Models (MGFMs) allow for leveraging the rich multimodal information in Multimodal-Attributed Graphs (MAGs)<n>We propose PLANET, a novel framework employing a Divide-and-Conquer strategy to decouple modality interaction and alignment across distinct granularities.<n>We show that PLANET significantly outperforms state-of-the-art baselines across diverse graph-centric and multimodal generative tasks.
  arXiv  Detail & Related papers  (2026-02-04T01:05:12Z)
• From Sparse Decisions to Dense Reasoning: A Multi-attribute Trajectory Paradigm for Multimodal Moderation [59.27094165576015]
  We propose a novel learning paradigm (UniMod) that transitions from sparse decision-making to dense reasoning traces.<n>By constructing structured trajectories encompassing evidence grounding, modality assessment, risk mapping, policy decision, and response generation, we reformulate monolithic decision tasks into a multi-dimensional boundary learning process.<n>We introduce specialized optimization strategies to decouple task-specific parameters and rebalance training dynamics, effectively resolving interference between diverse objectives in multi-task learning.
  arXiv  Detail & Related papers  (2026-01-28T09:29:40Z)
• MMRAG-RFT: Two-stage Reinforcement Fine-tuning for Explainable Multi-modal Retrieval-augmented Generation [31.90681057778075]
  Multi-modal Retrieval-Augmented Generation (MMRAG) enables highly credible generation by integrating external multi-modal knowledge.<n>Existing MMRAG methods fail to clarify the reasoning logic behind retrieval and response generation.
  arXiv  Detail & Related papers  (2025-12-19T03:19:54Z)
• Multi-Aspect Cross-modal Quantization for Generative Recommendation [27.92632297542123]
  We propose Multi-Aspect Cross-modal quantization for generative Recommendation (MACRec)<n>We first introduce cross-modal quantization during the ID learning process, which effectively reduces conflict rates.<n>We also incorporate multi-aspect cross-modal alignments, including the implicit and explicit alignments.
  arXiv  Detail & Related papers  (2025-11-19T04:55:14Z)
• 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)
• Principled Multimodal Representation Learning [70.60542106731813]
  Multimodal representation learning seeks to create a unified representation space by integrating diverse data modalities.<n>Recent advances have investigated the simultaneous alignment of multiple modalities, yet several challenges remain.<n>We propose Principled Multimodal Representation Learning (PMRL), a novel framework that achieves simultaneous alignment of multiple modalities.
  arXiv  Detail & Related papers  (2025-07-23T09:12:25Z)
• Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations [43.07575348801021]
  Domain Generalization (DG) aims to enhance model robustness in unseen or distributionally shifted target domains through training exclusively on source domains.<n>A key challenge in Multi-modal Domain Generalization (MMDG) has emerged: enabling models trained on multi-modal sources to generalize to unseen target distributions within the same modality set.<n>We propose a novel approach that leverages Unified Representations to map different paired modalities together.
  arXiv  Detail & Related papers  (2025-07-04T05:17:32Z)
• Gated Multimodal Graph Learning for Personalized Recommendation [9.466822984141086]
  Multimodal recommendation has emerged as a promising solution to alleviate the cold-start and sparsity problems in collaborative filtering.<n>We propose RLMultimodalRec, a lightweight and modular recommendation framework that combines graph-based user modeling with adaptive multimodal item encoding.
  arXiv  Detail & Related papers  (2025-05-30T16:57:17Z)
• UniversalRAG: Retrieval-Augmented Generation over Multiple Corpora with Diverse Modalities and Granularities [53.76854299076118]
  UniversalRAG is a novel RAG framework designed to retrieve and integrate knowledge from heterogeneous sources with diverse modalities and granularities. We propose a modality-aware routing mechanism that dynamically identifies the most appropriate modality-specific corpus and performs targeted retrieval within it. We validate UniversalRAG on 8 benchmarks spanning multiple modalities, showing its superiority over modality-specific and unified baselines.
  arXiv  Detail & Related papers  (2025-04-29T13:18:58Z)
• Disentangling and Generating Modalities for Recommendation in Missing Modality Scenarios [21.73914052076956]
  We propose Disentangling and Generating Modality Recommender (DGMRec) for missing modality scenarios. DGMRec disentangles modality features into general and specific modality features from an information-based perspective. It consistently outperforms state-of-the-art MRSs in challenging scenarios.
  arXiv  Detail & Related papers  (2025-04-23T02:04:14Z)
• Towards Modality Generalization: A Benchmark and Prospective Analysis [56.84045461854789]
  This paper introduces Modality Generalization (MG), which focuses on enabling models to generalize to unseen modalities.<n>We propose a comprehensive benchmark featuring multi-modal algorithms and adapt existing methods that focus on generalization.<n>Our work provides a foundation for advancing robust and adaptable multi-modal models, enabling them to handle unseen modalities in realistic scenarios.
  arXiv  Detail & Related papers  (2024-12-24T08:38:35Z)
• CUE-M: Contextual Understanding and Enhanced Search with Multimodal Large Language Model [9.224965304457708]
  This paper introduces Contextual Understanding and Enhanced Search with MLLM (CUE-M), a novel multimodal search framework.<n>It incorporates image context enrichment, intent refinement, contextual query generation, external API integration, and relevance-based filtering.<n>Experiments on real-word datasets and public benchmarks on knowledge-based VQA and safety demonstrated that CUE-M outperforms baselines and establishes new state-of-the-art results.
  arXiv  Detail & Related papers  (2024-11-19T07:16:48Z)
• Towards Bridging the Cross-modal Semantic Gap for Multi-modal Recommendation [12.306686291299146]
  Multi-modal recommendation greatly enhances the performance of recommender systems. Most existing multi-modal recommendation models exploit multimedia information propagation processes to enrich item representations. We propose a novel framework to bridge the semantic gap between modalities and extract fine-grained multi-view semantic information.
  arXiv  Detail & Related papers  (2024-07-07T15:56:03Z)
• LLM4Rerank: LLM-based Auto-Reranking Framework for Recommendations [51.76373105981212]
  Reranking is a critical component in recommender systems, playing an essential role in refining the output of recommendation algorithms.<n>We introduce a comprehensive reranking framework, designed to seamlessly integrate various reranking criteria.<n>A customizable input mechanism is also integrated, enabling the tuning of the language model's focus to meet specific reranking needs.
  arXiv  Detail & Related papers  (2024-06-18T09:29:18Z)
• Retrieval Meets Reasoning: Even High-school Textbook Knowledge Benefits Multimodal Reasoning [49.3242278912771]
  We introduce a novel multimodal RAG framework named RMR (Retrieval Meets Reasoning) The RMR framework employs a bi-modal retrieval module to identify the most relevant question-answer pairs. It significantly boosts the performance of various vision-language models across a spectrum of benchmark datasets.
  arXiv  Detail & Related papers  (2024-05-31T14:23:49Z)
• SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization [13.456240733175767]
  SimMMDG is a framework to overcome the challenges of achieving domain generalization in multi-modal scenarios. We employ supervised contrastive learning on the modality-shared features to ensure they possess joint properties and impose distance constraints. Our framework is theoretically well-supported and achieves strong performance in multi-modal DG on the EPIC-Kitchens dataset and the novel Human-Animal-Cartoon dataset.
  arXiv  Detail & Related papers  (2023-10-30T17:58:09Z)
• Exploiting Modality-Specific Features For Multi-Modal Manipulation Detection And Grounding [54.49214267905562]
  We construct a transformer-based framework for multi-modal manipulation detection and grounding tasks. Our framework simultaneously explores modality-specific features while preserving the capability for multi-modal alignment. We propose an implicit manipulation query (IMQ) that adaptively aggregates global contextual cues within each modality.
  arXiv  Detail & Related papers  (2023-09-22T06:55:41Z)
• Abstractive Sentence Summarization with Guidance of Selective Multimodal Reference [3.505062507621494]
  We propose a Multimodal Hierarchical Selective Transformer (mhsf) model that considers reciprocal relationships among modalities. We evaluate the generalism of proposed mhsf model with the pre-trained+fine-tuning and fresh training strategies.
  arXiv  Detail & Related papers  (2021-08-11T09:59:34Z)

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.