There are some writing errors in our methods section that need to be corrected. We will then add extensive experiments and rewrite the Introduction and related work sections
As a paradigm that delves into the deep seated drivers of user behavior, motivation-based recommendation systems have emerged as a prominent research direction in the field of personalized information retrieval. Unlike traditional approaches that primarily rely on surface level interaction signals, these systems aim to uncover the intrinsic psychological factors that shape users' decision-making processes and content preferences. By modeling motivation, recommender systems can better interpret not only what users choose, but why they make such choices, thereby enhancing both the interpretability and the persuasive power of recommendations. However, existing studies often simplify motivation as a latent variable learned implicitly from behavioral data, which limits their ability to capture the semantic richness inherent in user motivations. In particular, heterogeneous information such as review texts which often carry explicit motivational cues remains underexplored in current motivation modeling frameworks. Extensive experiments conducted on three real world datasets demonstrate the effectiveness of the proposed LMMRec framework.
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）
Leveraging Multimodal Data and Side Users for Diffusion Cross-Domain Recommendation
School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications（北京邮电大学计算机学院（国家级试点软件工程学院））；The State Key Laboratory of Blockchain and Data Security, Zhejiang University（浙江大学区块链与数据安全国家重点实验室）；Central South University（中南大学）；National University of Singapore（新加坡国立大学）；Jinan University（暨南大学）
LLM4Rec: Large Language Models for Multimodal Generative Recommendation with Causal Debiasing
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；Peking University Changsha, China
Imagery as Inquiry: Exploring A Multimodal Dataset for Conversational Recommendation
University of California, San Diego（加利福尼亚大学圣迭戈分校）；Institute for Clarity in Documentation（文档清晰度研究所）；Inria Paris-Rocquencourt（法国国家信息与自动化研究所巴黎-罗夸恩库尔特中心）；Rajiv Gandhi University（拉吉夫·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕尔默研究实验室）
City University of Hong Kong（香港城市大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Michigan State University（密歇根州立大学）；Shanghai Jiaotong University（上海交通大学）；Jinan University（暨南大学）；The University of Queensland（昆士兰大学）
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）
Gated Multimodal Graph Learning for Personalized Recommendation
Miami Herbert Business School, University of Miami, FL, United States（迈阿密赫伯特商学院，迈阿密大学，佛罗里达州，美国）；School of Engineering, University of California, California, US（加州大学工程学院，加州，美国）；Department of Electrical & Computer Engineering, Rutgers University, Sunnyvale, United States（新泽西理工学院电子与计算机工程系，索菲亚维尔，美国）；Department of Electrical and Computer Engineering, Duke University, NC, United States（杜克大学电子与计算机工程系，北卡罗来纳州，美国）；School of Engineering, Computer & Mathematical Sciences (ECMS), Auckland University of Technology, Auckland, New Zealand（奥克兰理工大学工程学院，计算机与数学科学（ECMS）系，奥克兰，新西兰）；Department Of Computer Science, Universiti Putra Malaysia, Kuala Lumpur, Malaysia（马来西亚国立大学计算机科学系，吉隆坡，马来西亚）
Revisiting Content-Based Music Recommendation: Efficient Feature Aggregation from Large-Scale Music Models
School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；National Key Laboratory for Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）
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）
