Recent advances in multimodal recommendation have demonstrated the effectiveness of incorporating visual and textual content into collaborative filtering. However, real-world deployments raise an increasingly important yet underexplored issue: trustworthiness. On modern e-commerce platforms, multimodal content can be misleading or unreliable (e.g., visually inconsistent product images or click-bait titles), injecting untrustworthy signals into multimodal representations and making existing recommenders brittle under modality corruption. In this work, we take a step towards trustworthy multimodal recommendation from both a method and an analysis perspective. First, we propose a plug-and-play modality-level rectification component that mitigates untrustworthy modality features by learning soft correspondences between items and multimodal features. Using lightweight projections and Sinkhorn-based soft matching, the rectification suppresses mismatched modality signals while preserving semantic consistency, and can be integrated into existing multimodal recommenders without architectural modifications. Second, we present two practical insights on interaction-level trustworthiness under noisy collaborative signals: (i) training-set pseudo interactions can help or hurt performance under noise depending on prior-signal alignment; and (ii) propagation-graph pseudo edges can also help or hurt robustness, as message passing may amplify misalignment. Extensive experiments on multiple datasets and backbones under varying corruption levels demonstrate improved robustness from modality rectification and validate the above interaction-level observations.
Carnegie Mellon University（卡内基梅隆大学）；University of California, Los Angeles（加州大学洛杉矶分校）；Columbia University（哥伦比亚大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Chicago（芝加哥大学）；Rice University（莱斯大学）
Well Begun is Half Done: Training-Free and Model-Agnostic Semantically Guaranteed User Representation Initialization for Multimodal Recommendation
The University of Hong Kong（香港大学）；Beijing Institute of Technology（北京理工大学）；Carnegie Mellon University（卡内基梅隆大学）；Peking University（北京大学）；Macao Polytechnic University（澳门理工学院）
Multimodal-enhanced Federated Recommendation: A Group-wise Fusion Approach
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University（吉林大学教育部符号计算与知识工程重点实验室）；PolyU Academy for Artificial Intelligence, Hong Kong Polytechnic University（香港理工大学PolyU人工智能学院）；Australian Artificial Intelligence Institute, FEIT, University of Technology Sydney（悉尼科技大学FEIT澳大利亚人工智能研究所）；College of Computer Science, Inner Mongolia University（内蒙古大学计算机学院）
Agents as Knowledge Integrator and Utilizer in Multimodal Recommendation
The University of Hong Kong（香港大学）；Beijing Institute of Technology（北京理工大学）；Peking University（北京大学）；Universiti Malaya（马来亚大学）；Macao Polytechnic University（澳门理工学院）
MLLMRec-R1: Incentivizing Reasoning Capability in Large Language Models for Multimodal Sequential Recommendation
Hefei University of Technology（合肥工业大学）；National University of Singapore（新加坡国立大学）；University of Science and Technology of China（中国科学技术大学）；China Academy of Electronic and Information Technology（中国电子信息产业集团有限公司）
Seeing and Reflecting: Multimodal Memory-Enhanced Agent Collaboration for Recommendation
Tsinghua University（清华大学）；University of Science and Technology of China（中国科学技术大学）；Peking University（北京大学）；The University of New South Wales（新南威尔士大学）；Macquarie University（麦考瑞大学）；CSIRO’s Data61（联邦科学与工业研究组织数据61）
