LEADER 12735nam 22008295 450 001 9910851987103321 005 20251225200654.0 010 $a9789819722624 010 $a9819722624 024 7 $a10.1007/978-981-97-2262-4 035 $a(CKB)31801751800041 035 $a(MiAaPQ)EBC31305371 035 $a(Au-PeEL)EBL31305371 035 $a(DE-He213)978-981-97-2262-4 035 $a(MiAaPQ)EBC31574360 035 $a(Au-PeEL)EBL31574360 035 $a(EXLCZ)9931801751800041 100 $a20240424d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in Knowledge Discovery and Data Mining $e28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7?10, 2024, Proceedings, Part V /$fedited by De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin 205 $a1st ed. 2024. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024. 215 $a1 online resource (431 pages) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v14649 311 08$a9789819722648 311 08$a9819722640 320 $aIncludes bibliographical references and index. 327 $aIntro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part V -- Multimedia and Multimodal Data -- Re-thinking Human Activity Recognition with Hierarchy-Aware Label Relationship Modeling -- 1 Introduction -- 2 Related Work -- 2.1 Human Activity Recognition (HAR) -- 2.2 Hierarchical Label Modeling -- 3 Problem Formulation -- 4 Our Proposals -- 4.1 Hierarchy-Aware Label Encoding -- 4.2 Activity Data Encoding -- 4.3 Label-Data Joint Embedding Learning -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Experimental Results -- 5.3 Ablation Study -- 6 Discussions and Conclusion -- References -- Geometrically-Aware Dual Transformer Encoding Visual and Textual Features for Image Captioning -- 1 Introduction -- 2 Related Works -- 3 Proposed Approach -- 3.1 Features Extractor -- 3.2 Caption Generator -- 3.3 Attention Block -- 3.4 Training and Objectives -- 4 Experiments -- 4.1 Experiments Setup -- 4.2 Experiment Result -- 5 Conclusions -- References -- MHDF: Multi-source Heterogeneous Data Progressive Fusion for Fake News Detection -- 1 Introduction -- 2 Related Work -- 3 MHDF Model -- 3.1 Model Overview -- 3.2 Multi-source Heterogeneous Data Amplification -- 3.3 News Textual Feature Fusion -- 3.4 News Visual Feature Fusion -- 3.5 Sentiment Feature Extractor -- 3.6 Feature Integration Classifier -- 4 Experiments -- 4.1 Dataset -- 4.2 Experimental Settings -- 4.3 Performance Comparison -- 4.4 Ablation Experiments and Validity Verification -- 4.5 Conclusions -- References -- Accurate Semi-supervised Automatic Speech Recognition via Multi-hypotheses-Based Curriculum Learning -- 1 Introduction -- 2 Related Works -- 2.1 Automatic Speech Recognition Methods -- 2.2 Connectionist Temporal Classification (CTC) Loss -- 3 Proposed Method -- 3.1 Multiple Hypotheses for Unlabeled Instances. 327 $a3.2 Training ASR Model with Multiple Hypotheses -- 3.3 Curriculum Learning -- 3.4 Theoretical Analysis -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Transcription Performance (Q1) -- 4.3 Speed of Convergence (Q2) -- 4.4 Ablation Study (Q3) -- 5 Conclusions -- References -- MM-PhyQA: Multimodal Physics Question-Answering with Multi-image CoT Prompting -- 1 Introduction -- 2 Related Works -- 2.1 Available Datasets -- 2.2 Large Multimodal Models and Chain-of-Thought -- 3 Novel Dataset -- 3.1 Original Dataset Creation -- 3.2 Data Augmentation Procedure -- 3.3 Chain of Thought Variant -- 3.4 MM-PhyQA Dataset Topics -- 4 Methodology -- 4.1 Multi-image Chain-of-Thought (MI-CoT) -- 5 Experiments -- 5.1 Models -- 6 Results and Discussion -- 6.1 Model Performance -- 6.2 Zero Shot Prompting Vs Supervised Fine-Tuning -- 6.3 Effect of Chain of Thought Prompting -- 6.4 Error Analysis -- 7 Conclusion -- References -- Adversarial Text Purification: A Large Language Model Approach for Defense -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Large Language Models -- 3.2 Adversarial Text Purification -- 4 LLM-Guided Adversarial Text Purification -- 5 Experiments -- 5.1 Experimental Setting -- 5.2 Results and Discussion -- 6 Conclusion -- References -- lil'HDoC: An Algorithm for Good Arm Identification Under Small Threshold Gap -- 1 Introduction -- 2 Background -- 2.1 Good Arm Identification -- 3 Problem Setting -- 4 Preliminary -- 5 Algorithm -- 5.1 Correctness of lil'HDoC -- 5.2 First Arms Sampling Complexity -- 5.3 Total Sample Complexity -- 6 Experiment -- 6.1 Dataset -- 6.2 Baseline -- 6.3 Results -- 7 Conclusion -- References -- Recommender Systems -- ScaleViz: Scaling Visualization Recommendation Models on Large Data -- 1 Introduction -- 2 Related Works -- 3 Problem Formulation -- 4 Proposed Solution -- 4.1 Cost Profiling -- 4.2 RL Agent. 327 $a5 Evaluations -- 5.1 Experimental Setup -- 5.2 Speed-Up in Visualization Generation -- 5.3 Budget vs. Error Trade-Off -- 5.4 Need for Dataset-Specific Feature Selection -- 5.5 Scalability with Increasing Data Size -- 6 Conclusion -- References -- Collaborative Filtering in Latent Space: A Bayesian Approach for Cold-Start Music Recommendation -- 1 Introduction -- 2 Related Work and Problem Formulation -- 2.1 Problem Formulation -- 3 Methodology -- 3.1 Overview -- 3.2 Statistical Model in CFLS -- 3.3 Optimization -- 3.4 Prediction -- 4 Experiments -- 4.1 Dataset -- 4.2 Experimental Settings -- 4.3 Performance Comparisons -- 4.4 Influence of Different Cold-Start Levels -- 4.5 Diversity, Interpretability and User Controllability -- 5 Conclusions -- References -- On Diverse and Precise Recommendations for Small and Medium-Sized Enterprises -- 1 Introduction -- 2 Related Work -- 3 Definitions and Problem Statement -- 4 Variants of a Session-Based Recommender System -- 4.1 Quality Metrics -- 5 Experiments and Evaluation -- 5.1 Selection of Real-World Datasets -- 5.2 Task Definition and Parameter Configuration -- 5.3 Evaluation of Experimental Results -- 6 Conclusion and Future Work -- References -- HMAR: Hierarchical Masked Attention for Multi-behaviour Recommendation -- 1 Introduction -- 2 Methodology -- 2.1 Problem Formulation -- 2.2 HMAR -- 2.3 Multi-task Learning -- 3 Experiments -- 3.1 Experimental Settings -- 3.2 Evaluation Protocol -- 3.3 Model Performance (RQ1) -- 3.4 Effect of Auxiliary Behaviors and Individual Model Components (RQ2 & -- RQ3) -- 4 Related Work -- 5 Conclusion -- References -- Residual Spatio-Temporal Collaborative Networks for Next POI Recommendation -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Problem Formulation -- 3.2 Long-Term Dependence Module -- 3.3 Short-Term Dependence Module -- 3.4 Sample Balancer. 327 $a4 Experiments -- 4.1 Experimental Settings -- 4.2 Recommendation Performance -- 4.3 Ablation Study -- 5 Conclusions -- References -- Conditional Denoising Diffusion for Sequential Recommendation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Stepwise Diffuser -- 3.2 Sequence Encoder -- 3.3 Cross-Attentive Conditional Denoising Decoder -- 3.4 Optimization -- 4 Experiments -- 4.1 Plateau of Ranking Prediction -- 4.2 Overall Experiments -- 4.3 Ablation Study -- 4.4 Hyperparameter Sensitivity -- 4.5 Case Study for Stepwise Generation -- 5 Conclusion -- References -- UIPC-MF: User-Item Prototype Connection Matrix Factorization for Explainable Collaborative Filtering -- 1 Introduction -- 2 Related Work -- 2.1 Collaborative Filtering -- 2.2 Explainable and Transparent Recommender Models -- 2.3 The Prototype-Based Collaborative Filtering -- 3 Methodology -- 3.1 User-Item Prototypes Connections Matrix Factorization (UIPC-MF) -- 3.2 Loss Function -- 4 Experiments and Discussion -- 4.1 Evaluation Metrics -- 4.2 Baseline Models -- 4.3 Training Details -- 4.4 Evaluation Results -- 4.5 Explaining UIPC-MF Recommendations -- 4.6 The Impact of L1-Norm in Reduction of Learning Bias -- 5 Conclusion -- References -- Towards Multi-subsession Conversational Recommendation -- 1 Introduction -- 2 Related Works -- 3 MSMCR Scenario -- 3.1 Definition -- 3.2 General Framework -- 4 Methodology -- 4.1 Context-Aware Recommendation -- 4.2 Policy Learning -- 4.3 Model Training -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Overall Performance -- 5.3 Further Experiments -- 6 Conclusion -- References -- False Negative Sample Aware Negative Sampling for Recommendation -- 1 Introduction -- 2 Related Work -- 3 Preliminary -- 4 Methodology -- 4.1 False Negatives Identification -- 4.2 False Negatives Elimination -- 5 Experiment -- 5.1 Experiment Settings. 327 $a5.2 Performance Comparison -- 5.3 Study of EDNS -- 6 Conclusion -- References -- Multi-sourced Integrated Ranking with Exposure Fairness -- 1 Introduction -- 2 Problem Formulation -- 3 Proposed Model -- 3.1 Input Layer -- 3.2 Dual RNN Module -- 3.3 Multi-task Module -- 3.4 Model Training -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Baselines -- 4.3 Model Selection -- 4.4 Performance Comparison -- 4.5 Ablation Study -- 4.6 Online A/B Testing -- 5 Conclusion -- References -- Soft Contrastive Learning for Implicit Feedback Recommendations -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Notations -- 3.2 The SCLRec Framework -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Overall Performance (RQ1) -- 4.3 Ablation Study (RQ2) -- 4.4 Robustness to Interaction Noises (RQ3) -- 5 Conclusion -- References -- Dual-Graph Convolutional Network and Dual-View Fusion for Group Recommendation -- 1 Introduction -- 2 Problem Formulation -- 3 Approach -- 3.1 Dual-Graph Construction -- 3.2 Dual-Graph Network for Member Preference -- 3.3 Dual-View Fusion for Group Preference -- 3.4 Group Recommendation and Model Training -- 4 Experiments -- 4.1 Experimental Dataset and Setup -- 4.2 Experimental Results and Analysis -- 4.3 Parameter Sensitivity -- 5 Related Works -- 6 Conclusion and Future Work -- References -- TripleS: A Subsidy-Supported Storage for Electricity with Self-financing Management System -- 1 Introduction -- 2 Literature Review -- 2.1 Electricity Subsidy and Operating Reserve -- 2.2 Electricity Management System -- 2.3 Electricity Storage -- 3 Problem Definition and Simulation Environment -- 4 Proposed TripleS -- 5 Experimental Results -- 5.1 Performance Evaluation -- 5.2 Performance Evaluation Under MS Attack -- 5.3 Influence of Self-discharge -- 6 Conclusion -- References -- Spatio-temporal Data. 327 $aMask Adaptive Spatial-Temporal Recurrent Neural Network for Traffic Forecasting. 330 $aThe 6-volume set LNAI 14645-14650 constitutes the proceedings of the 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, which took place in Taipei, Taiwan, during May 7?10, 2024. The 177 papers presented in these proceedings were carefully reviewed and selected from 720 submissions. They deal with new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, big data technologies, and foundations. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v14649 606 $aArtificial intelligence 606 $aAlgorithms 606 $aEducation$xData processing 606 $aComputer science$xMathematics 606 $aSignal processing 606 $aComputer networks 606 $aArtificial Intelligence 606 $aDesign and Analysis of Algorithms 606 $aComputers and Education 606 $aMathematics of Computing 606 $aSignal, Speech and Image Processing 606 $aComputer Communication Networks 615 0$aArtificial intelligence. 615 0$aAlgorithms. 615 0$aEducation$xData processing. 615 0$aComputer science$xMathematics. 615 0$aSignal processing. 615 0$aComputer networks. 615 14$aArtificial Intelligence. 615 24$aDesign and Analysis of Algorithms. 615 24$aComputers and Education. 615 24$aMathematics of Computing. 615 24$aSignal, Speech and Image Processing. 615 24$aComputer Communication Networks. 676 $a006.3 702 $aYang$b De-Nian 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910851987103321 996 $aAdvances in Knowledge Discovery and Data Mining$9772012 997 $aUNINA