Advances in Knowledge Discovery and Data Mining

Big data.- Toward Explainable Recommendation Via Counterfactual Reasoning.- Online Volume Optimization for Notifications via Long Short-Term Value Modeling.- Discovering Geo-referenced Frequent Patterns in Uncertain Geo-referenced Transactional Databases.- Financial data.- Joint Latent Topic Discovery and Expectation Modeling for Financial Markets.- Let the model make financial senses: a Text2Text generative approach for financial complaint identification.- Information retrieval and search.- Web-scale Semantic Product Search With Large Language Models.- Multi-task learning based Keywords weighted Siamese Model for semantic retrieval.- Relation-Aware Network with Attention-Based Loss for Few-Shot Knowledge Graph Completion.- MFBE: Leveraging Multi-Field Information of FAQs for Efficient Dense Retrieval.- Isotropic Representation Can Improve Dense Retrieval.- Knowledge-Enhanced Prototypical Network with Structural Semantics forFew-Shot Relation Classification.- Internet of Things.- MIDFA : Memory-Based Instance Division and Feature Aggregation Network for Video Object Detection.- Medical and biological data.- Vision Transformers for Small Histological Datasets learned through Knowledge Distillation.- Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis.- DKFM: Dual Knowledge-guided Fusion Model for Drug Recommendation.- Hierarchical Graph Neural Network for Patient Treatment Preference Prediction with External Knowledge.- Multimedia and multimodal data.- An Extended Variational Mode Decomposition Algorithm Developed Speech Emotion Recognition Performance.- Dynamically-Scaled Deep Canonical Correlation Analysis.- TCR: Short Video Title Generation and Cover Selection with Attention Refinement.- ItrievalKD: An Iterative Retrieval Framework Assisted with Knowledge Distillation for Noisy Text-to-Image Retrieval.- Recommender systems.- Semantic Relation Transfer for Non-overlapped Cross-domain Recommendations.- Interest Driven Graph Structure Learning for Session-Based Recommendation.- Multi-behavior Guided Temporal Graph Attention Network for Recommendation.- Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for Top-N Recommendation.- Meta-learning Enhanced Next POI Recommendation by Leveraging Check-ins from Auxiliary Cities.- Global-Aware External Attention Deep Model for Sequential Recommendation.- Aggregately Diversified Bundle Recommendation via Popularity Debiasing and Configuration-aware Reranking.- Diversely Regularized Matrix Factorization for Accurate and Aggregately Diversified Recommendation.- kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval.- Staying or Leaving: A Knowledge-Enhanced User Simulator for Reinforcement Learning Based Short Video Recommendation.- RLMixer: A Reinforcement Learning Approach For Integrated Ranking With Contrastive User Preference Modeling.