- Recommender System.- Hierarchical Review-based Recommendation with Contrastive Collaboration.- Adaptive Augmentation and Neighbor Contrastive Learning for Multi-Behavior Recommendation.- Automated Modeling of Influence Diversity with Graph Convolutional Network for Social Recommendation.- Contrastive Generator Generative Adversarial Networks for Sequential Recommendation.- Distribution-aware Diversification for Personalized Re-ranking in Recommendation.- KMIC: A Knowledge-aware Recommendation with Multivariate Intentions Contrastive Learning.- Logic Preference Fusion Reasoning on Recommendation.

- MHGNN: Hybrid Graph Neural Network with Mixers for Multi-interest Session-aware Recommendation.- Mixed Augmentation Contrastive Learning for Graph Recommendation System.- Noise-Resistant Graph Neural Networks for Session-based Recommendation.- S2DNMF: A Self-supervised Deep Nonnegative Matrix Factorization Recommendation Model Incorporating Deep Latent Features of Network Structure.- Self-Filtering Residual Attention Network based on Multipair Information Fusion for Session-Based Recommendations.- TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback.- VM-Rec: A Variational Mapping Approach for Cold-start User Recommendation.- Knowledge Graph.

- Matching Tabular Data to Knowledge Graph based on Multi-level Scoring Filters for Table Entity Disambiguation.- Complex Knowledge Base Question Answering via Structure and Content Dual-driven Method.- EvoREG: Evolutional Modeling with Relation-Entity Dual-Guidance for Temporal Knowledge Graph Reasoning.- Federated Knowledge Graph Embedding Unlearning via Diffusion Model.- Functional Knowledge Graph Towards Knowledge Application and Data Management for General Users.- Hospital Outpatient Guidance System Based On Knowledge Graph.- TOP: Taxi Destination Prediction Based on Trajectory Knowledge Graph.- Type-based Neighborhood Aggregation for Knowledge Graph Alignment.

- An Aggregation Procedure Enhanced Mechanism for GCN-based Knowledge Graph Completion Model by Leveraging Condensed Sampling and Attention Optimization.- Spatial and Temporal Data.- Capturing Fine and Coarse Grained User Preferences with Dual-Transformer for Next POI Recommendation.- Enhancing Spatio-Temporal Semantics with Contrastive Learning for Next POI Recommendation.- Distinguish the Indistinguishable: Spatial Personalized Transformer for Traffic Flow Forecast.- Meeting Pattern Detection from Trajectories in Road Network.- Speed Prediction of Multiple Traffic Scenarios with Local Fluctuation.- ST-TPFL: Towards Spatio-Temporal Traffic Flow Prediction Based on Topology Protected Federated Learning.

- A Context-aware Distance Analysis Approach for Time Series.- Dual-view Stack State Learning Network for Attribute-based Container Location Assignment.- Efficient Coverage Query over Transition Trajectories.