Turning Semantics into Topology: LLM-Driven Attribute Augmentation for Collaborative Filtering
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）；Alibaba Group（阿里巴巴集团）；Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；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）
Large Language Models (LLMs) have shown great potential for enhancing recommender systems through their extensive world knowledge and reasoning capabilities. However, effectively translating these semantic signals into traditional collaborative embeddings remains an open challenge. Existing approaches typically fall into two extremes: direct inference methods are computationally prohibitive for large-scale retrieval, while embedding-based methods primarily focus on unilateral feature augmentation rather than holistic collaborative signal enhancement. To bridge this gap, we propose Topology-Augmented Graph Collaborative Filtering (TAGCF), a novel framework that transforms semantic knowledge into topological connectivity. Unlike existing approaches that depend on textual features or direct interaction synthesis, TAGCF employs LLMs to infer interaction intents and underlying causal relationships from user-item pairs, representing these insights as intermediate attribute nodes within an enriched User-Attribute-Item (U-A-I) graph. Furthermore, to effectively model the heterogeneous relations in this augmented structure, we propose Adaptive Relation-weighted Graph Convolution (ARGC), which employs relation-specific prediction networks to dynamically estimate the importance of each relation type. Extensive experiments across multiple benchmark datasets and CF backbones demonstrate consistent improvements, with comprehensive evaluations including cold-start scenarios validating the effectiveness and robustness of our framework. All code will be made publicly available. For anonymous review, our code is available at the following anonymous link: https://anonymous.4open.science/r/AGCF-2441353190/.
Quality-Aware Collaborative Multi-Positive Contrastive Learning for Sequential Recommendation
Shandong University of Science and Technology（山东科技大学）；Zhejiang Lab（浙江实验室）；School of Information Science and Engineering, Shandong University（山东大学信息科学与工程学院）；Faculty of Engineering and Information Technology, University of Technology Sydney（悉尼科技大学工程与信息科技学院）；School of Computer Science and Technology, Shandong University（山东大学计算机科学与技术学院）；School of Computer Science and Engineering, Shandong University of Science and Technology（山东科技大学计算机科学与工程学院）
Integrating Large Language Models into Recommendation via Mutual Augmentation and Adaptive Aggregation
Dongguan University of Technology（东莞理工学院）；City University of Hong Kong（香港城市大学）；City University of Hong Kong Shenzhen Research Institute（香港城市大学深圳研究院）；Tsinghua University（清华大学）；Chinese University of Hong Kong（香港中文大学）；University of Hawaii（夏威夷大学）；Capital University of Economics and Business（首都经济贸易大学）；SenseTime Research（商汤科技研究院）
RePair: Turning Retrieval Failures into Counterfactual Hard Pairs
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Tencent Yuanbao（腾讯元宝）；The University of Hong Kong（香港大学）；ARC Lab, Tencent（腾讯ARC实验室）
