Traditional sequential recommendation (SR) models learn low-dimensional item ID embeddings from user-item interactions, often overlooking textual information such as item titles or descriptions. Recent advances in Large Language Models (LLMs) have inspired a surge of research that encodes item textual information with high-dimensional semantic embeddings, and designs transformation methods to inject such embeddings into SR models. These embedding transformation strategies can be categorized into two types, both of which exhibits notable drawbacks: 1) adapter-based methods suffer from pronounced dimension collapse, concentrating information into a few dominant dimensions; 2) SVD-based methods are rigid and manual, considering only a few principal spectral components while discarding rich information in the remaining spectrum.
To address these limitations, we propose SpecTran, a spectral-aware transformer-based adapter that operates in the spectral domain, attending to the full spectrum to select and aggregates informative components. A learnable spectral-position encoding injects singular-value cues as an inductive bias, guiding attention toward salient spectral components and promoting diversity across embedding dimensions. Across four real-world datasets and three SR backbones, it consistently outperforms strong baselines, achieving an average improvement of 9.17%.
TransFR: Transferable Federated Recommendation with Adapter Tuning on Pre-trained Language Models
Key Laboratory of Big Data & Artificial Intelligence in Transportation, Ministry of Education, China（交通运输部大数据与人工智能重点实验室）；Beijing Jiaotong University, China（北京交通大学）；University of Technology Sydney, Australia（悉尼科技大学）；Peking University, China（北京大学）；Nanyang Technological University, Singapore（南洋理工大学）
CDR-Adapter: Learning Adapters to Dig Out More Transferring Ability for Cross-Domain Recommendation Models
Tsinghua-Berkeley Shenzhen Institute, Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院清华-伯克利深圳学院）；Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Ant Group（蚂蚁集团）
Break the ID-Language Barrier: An Adaption Framework for LLM-based Sequential Recommendation
Huawei, Beijing, China（华为，北京，中国）；Institute of Finance Technology, UCL, United Kingdom（金融技术研究所，UCL，英国）；Civil, Environmental and Geomatic Engineering, UCL, United Kingdom（土木、环境与地理工程，UCL，英国）
Federated Semantic Residuals for Multimodal Recommendation
Australian Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology Sydney（悉尼科技大学工程与信息学院澳大利亚人工智能研究所）；Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University（香港理工大学数据科学与人工智能系）
Bridge the Domains: Large Language Models Enhanced Cross-domain Sequential Recommendation
Xi'an Jiaotong University \& City University of Hong Kong Xi'an China；City University of Hong Kong Hong Kong China；Jilin University Changchun China；Tsinghua University Beijing China；Nanyang Technological University Singapore Singapore；Independent Researcher Beijing China；Xi'an Jiaotong University Xi'an China；Xi'an Jiaotong University \& City University of Hong Kong；City University of Hong Kong；Jilin University；Tsinghua University；Nanyang Technological University；Independent Researcher；Xi'an Jiaotong University
RAIE: Region-Aware Incremental Preference Editing with LoRA for LLM-based Recommendation
School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区计算机科学与技术学院）；Department of Computer Science and Technology, Xiamen University（厦门大学计算机科学与技术系）；Artificial Intelligence Research Institute, Shenzhen MSU-BIT University（深圳MSU-BIT大学人工智能研究院）；School of Information, Central University of Finance and Economics（中央财经大学信息学院）；School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）
Bridging Collaborative Filtering and Large Language Models with Dynamic Alignment, Multimodal Fusion and Evidence-grounded Explanations
Department of Software \& Microelectronics, Peking University, Beijing, China；Economic Law School, China University of Political Science；Financial Media, Peking University, ChangSha, China
