- Recommendation.- Hypergraph Enhanced Knowledge Tree Prompt Learning for Next-Basket Recommendation.- MHGCP:Multi-View Heterogeneous Graph with Cross-View Projection for Recommendation.- Towards Scenario-adaptive User Behavior Modeling for Multi-scenario Recommendation.- Leave No One Behind: Enhancing Diversity While Maintaining Accuracy in Social Recommendation.- Counterfactual Path Augmentation for Reinforcement Reasoning in Explainable Recommendation.- Adaptive Personalized Federated Recommendation with Global Knowledge Distillation.- FHCF: Fully-Hyperbolic Symmetric Graph Learning for Collaborative Filtering.

- UGDA: A Unified Graph-based Method with Domain-specific Adaptation for Multi-domain Recommendation.- Self-supervised Hierarchical Representation for Medication Recommendation.- Self-Supervised Dual Graph and Intention Association for Session-based Recommendation.- Exercise Recommendation Based on Feature-Aligned Knowledge Tracing.- Joint User and Item Prototype Alignment for Cross-Platform Recommendation.- Diffusion Multi-Behavior Recommender Model .- HHGCN-DrugRec: Hierarchical HyperGraph Convolution Network for Drug Combination Recommendation.- Emotion-based Conversational Recommendation by Inferring Implicit Users& Preferences from their Subjective Claims.

- CDIVR: Cognitive Dissonance-aware Interactive Video Recommendation.- Modeling Personalized Short-term and Periodic Long-term Preferences for Enhanced Next POI Recommendations.- DRE: Generating Recommendation Explanations by Aligning Large Language Models at Data-level.- Towards Unified Modeling for Positive and Negative Preferences in Sign-aware Recommendation.- Alignment-Uniformity Aware Feature Representation Learning for CTR Prediction.- Diffusion Based Data Augmentation for Multi-behavior Sequential Recommendation.- Semantic Gaussian Mixture Variational Autoencoder for Sequential Recommendation*.- Personalized Education with Ranking Alignment Recommendation.

- HierLLM: Hierarchical Large Language Model for Question Recommendation.- Comprehensive Interest Modeling and Relational Mining for Multi-modal Recommendation.- Demand-oriented Route Recommendation for Shared Mobility Services.- CoCoB: Adaptive Collaborative Combinatorial Bandits for Online Recommendation.- KG-TS: Knowledge Graph-driven Thompson Sampling for Online Recommendation.- Efficient Noise-reducing Neural Network for Cross-Domain Sequential Recommendation.- Bridging RDF Knowledge Graphs with Graph Neural Networks for Semantically-Rich Recommender Systems.- Security & Privacy.

- Lattice-based Forward Secure Certificateless Encryption Scheme for Cloud Data Management.- Logarithmic-size Lattice-based Linkable Ring Signature for Cloud Data Management.- CyberLLM: Enable Mapping CVE to Tactics and Techniques of Cyber Threats via LLM.- Privacy-preserving Multi-Dimensional Range Query Optimization Across Multiple Sources.- Decoupled Self-Knowledge Distillation Makes Differentially Private Deep Learning Stronger.- PriExRec: Defending Against Membership Inference Attacks in Federated Recommendation with Explicit Feedback.- OPOM: The Ordinal and Parallel Optimization Method of Spark multi-query applications.- Enabling Efficient and Authenticated Trajectory Similarity Retrieval on Blockchain-assisted Cloud.

- InC: A Vertical Federated Learning Framework with Multiple Noisy Labels.- Breaking Free from Label Limitations: A Novel Unsupervised Attack Method for Graph Classification.- TSALockMark: An Asymmetric and Robust Watermarking Scheme for Relational Databases with Distortion Constraints.- Towards Confidential and Efficient LLM Inference with Dual Privacy Protection.- ECPIR: Efficient and Controllable Privacy-Preserving Image Retrieval in Cloud-Assisted System.- Privacy-preserving Image Generation Based on Self-Attention.