MLLMRec：面向多模态推荐的图细化偏好推理范式

Computer Science > Information Retrieval arXiv:2508.15304 (cs) [Submitted on 21 Aug 2025 (v1), last revised 10 Sep 2026 (this version, v3)] Title:MLLMRec: A Preference Reasoning Paradigm with Graph Refinement for Multimodal Recommendation Authors:Yuzhuo Dang, Xin Zhang, Zhiqiang Pan, Yuxiao Duan, Wanyu Chen, Fei Cai, Honghui Chen View a PDF of the paper titled MLLMRec: A Preference Reasoning Paradigm with Graph Refinement for Multimodal Recommendation, by Yuzhuo Dang and 5 other authors View PDF HTML (experimental) Abstract:Multimodal recommendation combines the user historical behaviors with the modal features of items to capture the tangible user preferences, presenting superior performance compared to the conventional ID-based recommender systems. However, existing methods still encounter two key problems in the representation learning of users and items, respectively: (1) the initialization of multimodal user representations is either agnostic to historical behaviors or contaminated by irrelevant modal noise, and (2) the widely used KNN-based item-item graph contains noisy edges with low similarities and lacks audience co-occurrence relationships. To address such issues, we propose MLLMRec, a novel preference reasoning paradigm with graph refinement for multimodal recommendation. Specifically, on the one hand, the item images are first converted into high-quality semantic descriptions using a multimodal large language model (MLLM), thereby bridging the semantic gap between visual and textual modalities. Then, we construct a behavioral description list for each user and feed it into the MLLM to reason about the purified user preference profiles that contain the latent interaction intents. The reasoned profiles and the multimodal descriptions of items, together with their ID embeddings, are propagated over the user-item interaction graph to absorb the high-order collaborative signals. On the other hand, we develop the threshold-controlled denoising and topology-aware enhancement strategies to refine the suboptimal item-item graph, which are applied to both the multimodal and ID item representations to improve the accuracy of item representation learning. Extensive experiments on three publicly available datasets demonstrate that MLLMRec achieves the state-of-the-art performance. The source code is provided at this https URL. Subjects: Information Retrieval (cs.IR) Cite as: arXiv:2508.15304 [cs.IR] (or arXiv:2508.15304v3 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2508.15304 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yuzhuo Dang [view email] [v1] Thu, 21 Aug 2025 06:50:00 UTC (1,729 KB) [v2] Sat, 24 Jan 2026 15:45:14 UTC (3,395 KB) [v3] Thu, 10 Sep 2026 13:20:21 UTC (696 KB) Full-text links: Access Paper: View a PDF of the paper titled MLLMRec: A Preference Reasoning Paradigm with Graph Refinement for Multimodal Recommendation, by Yuzhuo Dang and 5 other authors View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.IR new | recent | 2025-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)