Recommendation systems have faced significant challenges in cold-start scenarios, where new items with a limited history of interaction need to be effectively recommended to users. Though multimodal data (e.g., images, text, audio, etc.) offer rich information to address this issue, existing approaches often employ simplistic integration methods such as concatenation, average pooling, or fixed weighting schemes, which fail to capture the complex relationships between modalities. Our study proposes a novel Mixture of Experts (MoE) framework for multimodal cold-start recommendation, named MAMEX, which dynamically leverages latent representation from different modalities. MAMEX utilizes modality-specific expert networks and introduces a learnable gating mechanism that adaptively weights the contribution of each modality based on its content characteristics. This approach enables MAMEX to emphasize the most informative modalities for each item while maintaining robustness when certain modalities are less relevant or missing. Extensive experiments on benchmark datasets show that MAMEX outperforms state-of-the-art methods in cold-start scenarios, with superior accuracy and adaptability. For reproducibility, the code has been made available on Github https://github.com/L2R-UET/MAMEX.
Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential Recommendation
University of Science and Technology of China（中国科学技术大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；School of Artificial Intelligence and Data Science, University of Science and Technology of China（中国科学技术大学人工智能与数据科学学院）；Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）人工智能学域）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科技大学计算机科学与工程学系）
Cross-Modal Attention Network with Dual Graph Learning in Multimodal Recommendation
Beijing University of Posts and Telecommunications（北京邮电大学）；National University of Singapore（新加坡国立大学）；Tianjin University of Technology（天津工业大学）；Beihang University（北航）；State Key Laboratory of CNS/ATM（国家空管重大科技专项实验室）；Aviation Data Communication Corporation（航空数据通信公司）
SynGR: Unleashing the Potential of Cross-Modal Synergy for Generative Recommendation
School of Artificial Intelligence, Beihang University, Beijing, China（北京航空航天大学人工智能学院）；School of Computer Science and Engineering, Beihang University, Beijing, China（北京航空航天大学计算机科学与工程学院）
GUME: Graphs and User Modalities Enhancement for Long-Tail Multimodal Recommendation
Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；University of the Chinese Academy of Sciences（中国科学院大学）；University of Kansas（堪萨斯大学）
ExplainRec: Towards Explainable Multi-Modal Zero-Shot Recommendation with Preference Attribution and Large Language Models
Department of Software \& Microelectronics Peking University Beijing, China；Department of Software \& Microelectronics Peking University Beijing, China hangli\；Department of Software \& Microelectronics Peking University Beijing, China zehua\；Department of Software \& Microelectronics Peking University Beijing, China xiaofan\；Economic Law School China University of Political Science；School of Computer Science Peking University Beijing, China
National University of Singapore Master of Industrial and Systems Engineering（新加坡国立大学工业与系统工程硕士）；East China Normal University Master of Library and Information Science（华东师范大学图书馆与信息科学硕士）；Shandong Normal University（山东师范大学）
Cold-start Sequential Recommendation via Meta Learner
University of Electronic Science and Technology of China（电子科技大学）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Institute of Automation and Artificial Intelligence Research, Chinese Academy of Sciences（中国科学院自动化与人工智能研究院）
UMAIR-FPS: User-aware Multi-modal Animation Illustration Recommendation Fusion with Painting Style
National Pilot School of Software, Yunnan University（云南大学国家示范性软件学院）；Yunnan Key Laboratory of Software Engineering（云南省软件工程重点实验室）；Institute of Management of Technology, National Yang Ming Chiao Tung University（国立阳明交通大学科技管理研究所）
Multimodality Invariant Learning for Multimedia-Based New Item Recommendation
Hefei University of Technology（合肥工业大学）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）；Academy of Cyber（网络空间研究院）；Institute of Artificial Intelligence（人工智能研究院）
GenRecEdit: Adapting Model Editing for Generative Recommendation with Cold-Start Items
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学全球化人工智能学院）；School of Information Technology and Management, University of International Business and Economics（国际经济贸易大学信息科学技术与管理学院）
Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendation
Rutgers University（罗格斯大学）；University of Science and Technology of China（中国科学技术大学）；University of Pennsylvania（宾夕法尼亚大学）；Bytedance Inc.（字节跳动公司）；Georgia Institute of Technology（佐治亚理工学院）；University of Pittsburgh（匹兹堡大学）；The Hong Kong University of Science and Technology（香港科技大学）
