Database Systems for Advanced Applications: 27th International Conference, DASFAA 2022, Virtual Event, April 11–14, 2022, Proceedings, Part II (Lecture Notes in Computer Science)

✍ Scribed by Arnab Bhattacharya (editor), Janice Lee Mong Li (editor), Divyakant Agrawal (editor), P. Krishna Reddy (editor), Mukesh Mohania (editor), Anirban Mondal (editor), Vikram Goyal (editor), Rage Uday Kiran (editor)

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 744
• Category: Library

✦ Synopsis

The three-volume set LNCS 13245, 13246 and 13247 constitutes the proceedings of the 26th International Conference on Database Systems for Advanced Applications, DASFAA 2022, held online, in April 2021.

The total of 72 full papers, along with 76 short papers, are presented in this three-volume set was carefully reviewed and selected from 543 submissions. Additionally, 13 industrial papers, 9 demo papers and 2 PhD consortium papers are included.

The conference was planned to take place in Hyderabad, India, but it was held virtually due to the COVID-19 pandemic.

✦ Table of Contents

General Chairs’ Preface
Program Chairs’ Preface
Organization
Contents – Part II
Recommendation Systems
MDKE: Multi-level Disentangled Knowledge-Based Embedding for Recommender Systems
1 Introduction
2 Preliminaries
3 Methodology
3.1 Item Content Extraction
3.2 User Preference Propagation
3.3 Graph Structural Embedding
3.4 Model Prediction and Training
3.5 Analysis of MDKE
4 Experiments
4.1 Experimental Settings
4.2 MDKE Performance
4.3 Impacts of Hyperparameters
4.4 Ablation Study
5 Related Work
5.1 Graph-Based Methods for Recommendation
5.2 Disentangled Representation Learning
6 Conclusion
References
M3-IB: A Memory-Augment Multi-modal Information Bottleneck Model for Next-Item Recommendation
1 Introduction
2 Related Work
3 Problem Definition
4 The Proposed Method
4.1 Memory Network Framework
4.2 Multi-modal Information Bottleneck Model
4.3 Implementation for Next-Item Recommendation
4.4 Model Learning and Complexity Analysis
5 Experiment
5.1 Experimental Setup
5.2 Model Comparison
5.3 Ablation Study
5.4 Hyper-Parameter Study
6 Conclusion
References
Fully Utilizing Neighbors for Session-Based Recommendation with Graph Neural Networks
1 Introduction
2 Related Work
3 Preliminaries
3.1 Problem Definition
3.2 Session Graph Construction
3.3 Graph Attention Diffusion
4 Methodology
4.1 Positional Graph Attention Aggregation Layer
4.2 Multi-head Graph Attention Diffusion Layer
4.3 Session Embedding Readout
4.4 Prediction and Training
5 Experiments
5.1 Datasets
5.2 Baselines and Evaluation Metrics
5.3 Implementation Details
5.4 Overall Comparison (RQ1)
5.5 Ablation Study
5.6 Hyper-parameter Analysis (RQ4)
6 Conclusion
References
Inter- and Intra-Domain Relation-Aware Heterogeneous Graph Convolutional Networks for Cross-Domain Recommendation
1 Introduction
2 Related Work
2.1 Graph-Based Recommendation
2.2 Cross-Domain Recommendation
3 Preliminaries
4 Proposed Framework
4.1 Graph Construction and Embedding
4.2 Relation-Aware GCN Layer
4.3 Gating Fusion Layer
4.4 Prediction Layer
4.5 Model Training
5 Experiments
5.1 Experimental Settings
5.2 RQ1: Performance Comparison
5.3 RQ2: Ablation Study
5.4 RQ3: Effect of Inter-domain Relations
5.5 RQ4: Parameter Analysis
6 Conclusion and Future Work
References
Enhancing Graph Convolution Network for Novel Recommendation
1 Introduction
2 Related Work
2.1 Graph Based Methods for Recommendation
2.2 Novel Recommendation
3 Our Proposed Model
3.1 Problem Formulation
3.2 Model Overview
3.3 Embedding Layer
3.4 Masking Layer
3.5 Graph Convolutional Layer
3.6 Negative Sampling Layer
3.7 Reconstruction Layer
3.8 Gated Fusion Layer
3.9 Training
4 Experiments
4.1 Experimental Settings
4.2 Main Results
5 Detailed Study
5.1 Ablation Study
5.2 Parameter Study
6 Conclusion
References
Knowledge-Enhanced Multi-task Learning for Course Recommendation
1 Introduction
2 Related Work
2.1 Knowledge Tracing
2.2 Personality and Learning
2.3 Course Recommendation
3 Model Description
3.1 Problem Definition
3.2 Our Framework
4 Experiments
4.1 Dataset
4.2 Evaluation Metrics
4.3 Compared Methods
4.4 Performance Comparison
5 Conclusion
References
Learning Social Influence from Network Structure for Recommender Systems
1 Introduction
2 Related Works
2.1 Social Recommendation
2.2 Generative Adversarial Learning in RS
3 Preliminary
3.1 Problem Statement
3.2 Construction of Multi-views
4 Our Proposed Model
4.1 Network Structure Learning Component
4.2 The Generative and Adversarial Process
4.3 The Optimized Loss Function
5 Experiments
5.1 Experimental Settings
5.2 Main Results
5.3 Experimental Analysis
6 Conclusion
References
PMAR: Multi-aspect Recommendation Based on Psychological Gap
1 Introduction
2 Related Work
3 Methodology
3.1 Overall Psychological Gap Module
3.2 Personalized Psychological Gap Module
3.3 Rating Prediction Module
3.4 Objective Function
4 Experiments
4.1 Experiment Setup
4.2 Overall Performance Comparison
4.3 Influence of the Number of Aspects
4.4 Case Study
5 Conclusions and Future Work
References
Meta-path Enhanced Lightweight Graph Neural Network for Social Recommendation
1 Introduction
2 Related Work
3 Methodology
3.1 Notation and Problem Formulation
3.2 Embedding Layer
3.3 Aggregation Layer
3.4 Enhancement Layer
3.5 Predicting Layer
3.6 Model Training
3.7 Complexity Analysis
4 Experiments
4.1 Experimental Settings
4.2 Overall Comparison (RQ1)
4.3 Ablation Experiments (RQ2)
4.4 Performance Comparison Under Different Sparsity (RQ3)
5 Conclusion
References
Intention Adaptive Graph Neural Network for Category-Aware Session-Based Recommendation
1 Introduction
2 Related Works
2.1 Session-Based Recommendation
2.2 Category Information in Recommendation
3 Preliminary
4 Methodology
4.1 Overall Architecture
4.2 Category-Aware Graph Construction
4.3 Intention-Adaptive Graph Neural Network
4.4 Embeddings Fusion and Prediction
5 Experiments
5.1 Experimental Setup
5.2 Experimental Results (RQ1)
5.3 Ablation Study (RQ2&3)
5.4 Hyper-parameters Study (RQ4)
6 Conclusion
References
Multi-view Multi-behavior Contrastive Learning in Recommendation
1 Introduction
2 Related Work
3 Methodology
3.1 Preliminaries
3.2 Framework of Multi-view Multi-behavior Recommendation
3.3 Multi-behavior Contrastive Learning
3.4 Multi-view Contrastive Learning
3.5 Behavior Distinction Contrastive Learning
3.6 Optimization
4 Experiments
4.1 Datasets
4.2 Competitors
4.3 Experimental Settings
4.4 Results of Multi-behavior Recommendation (RQ1)
4.5 Ablation Study (RQ2)
4.6 Results on Cold-Start Scenarios (RQ3)
4.7 Parameter Analyses (RQ4)
5 Conclusion
References
Joint Locality Preservation and Adaptive Combination for Graph Collaborative Filtering
1 Introduction
2 Related Work
2.1 Collaborative Filtering
2.2 GCN-Based Recommendation
3 Preliminaries
3.1 Recap
3.2 Empirical Explorations
4 Methodology
4.1 LaGCF
5 Experiment
5.1 Experimental Setup
5.2 Performance Comparison with SOTA Methods
5.3 Ablation Study
5.4 Hyper-parameter Sensitivity
6 Conclusion
References
Gated Hypergraph Neural Network for Scene-Aware Recommendation
1 Introduction
2 Preliminary
3 Methodology
3.1 Heterogeneous Scene Hypergraph
3.2 Embedding Layer
3.3 Gated Hypergraph Neural Network
3.4 Separable Score Function
4 Experiments
4.1 Experimental Setup
4.2 Experimental Results
5 Related Work
6 Conclusion
References
Hyperbolic Personalized Tag Recommendation
1 Introduction
2 Related Work
2.1 Personalized Tag Recommendation Methods
2.2 Hyperbolic Embedding
3 Preliminaries
3.1 Problem Description
3.2 Hyperbolic Embedding
4 HPTR Model
4.1 Objective Function
4.2 Optimization
5 Experiments and Analysis
5.1 Datasets and Evaluation Metrics
5.2 Experiment Settings
5.3 Performance Comparison
5.4 Parameters Sensitivity Analysis
6 Conclusion
References
Diffusion-Based Graph Contrastive Learning for Recommendation with Implicit Feedback
1 Introduction
2 Related Work
2.1 Graph-Based Recommendation
2.2 Graph Contrastive Learning
3 The Proposed Recommendation Model
3.1 Diffusion-Based Graph Augmentation
3.2 Graph Encoders
3.3 Self-supervised Contrastive Leaning
3.4 Multi-task Training
4 Experiments
4.1 Experimental Settings
4.2 Performance Comparison
4.3 Ablation Study
4.4 Hyper-parameter Study
5 Conclusion
References
Multi-behavior Recommendation with Two-Level Graph Attentional Networks
1 Introduction
2 Preliminaries
3 Methodology
3.1 Embedding Layer
3.2 Attention Based Graph Convolution Layer
3.3 Output Layer
3.4 Model Training
4 Experiments
4.1 Experimental Settings
4.2 Overall Performance
4.3 Ablation Study
5 Conclusion
References
Collaborative Filtering for Recommendation in Geometric Algebra
1 Introduction
2 Methodology
2.1 GACF2 and GACF3
2.2 Model Optimization
3 Experiments
3.1 Experimental Setup
3.2 Performance Study
4 Conclusion
References
Graph Neural Networks with Dynamic and Static Representations for Social Recommendation
1 Introduction
2 Problem Definition
3 The Proposed Framework
3.1 User and Item Embedding
3.2 Interaction Aggregation
3.3 Relational Graph Aggregation
3.4 Output Layer
3.5 Training
4 Experiments
4.1 Experimental Settings
4.2 Quantitative Results
5 Conclusion
References
Toward Paper Recommendation by Jointly Exploiting Diversity and Dynamics in Heterogeneous Information Networks
1 Introduction
2 Problem Statement
3 Our Proposed Model
3.1 Information Extraction
3.2 In-out Degree Meta-path Sampling
3.3 Heterogeneous Entity Representation Learning
3.4 Probability Prediction
4 Experiments
4.1 Datasets
4.2 Evaluation Metrics and Settings
4.3 Comparison Study
5 Conclusion
References
Enhancing Session-Based Recommendation with Global Context Information and Knowledge Graph
1 Introduction
2 Notations and Problem Statement
3 Method
3.1 Global Knowledge Graph
3.2 Similar Session Reference Circle
3.3 Making Recommendation and Model Training
4 Experiments
4.1 Datasets
4.2 Compared Models
4.3 Over Performance
4.4 Ablation Study
5 Conclusion
References
GISDCN: A Graph-Based Interpolation Sequential Recommender with Deformable Convolutional Network
1 Introduction
2 Proposed Method
2.1 Graph-Based Interpolation Module
2.2 Deformable Convolutional and Prediction Module
3 Experiments
3.1 Datasets and Processing
3.2 Baselines and Experimental Setup
3.3 Performance Comparison
3.4 Model Discussions
4 Conclusion
References
Deep Graph Mutual Learning for Cross-domain Recommendation
1 Introduction
2 Related Work
3 The Proposed Model
3.1 Overview
3.2 Parallel GNN
3.3 Recommendation
3.4 Mutual Regularization
3.5 Model Optimization
4 Experiment
5 Conclusions
References
Core Interests Focused Self-attention for Sequential Recommendation
1 Introduction
2 Methodology
2.1 Embedding Layer
2.2 Core Interests Focused Self-attention
2.3 Prediction Layer
3 Experiments
3.1 Settings
3.2 Recommendation Performance
3.3 Ablation Study
4 Conclusion
References
SAER: Sentiment-Opinion Alignment Explainable Recommendation
1 Introduction
2 The Proposed Model
3 Experiments
4 Conclusion and Future Work
References
Toward Auto-Learning Hyperparameters for Deep Learning-Based Recommender Systems
1 Introduction
2 The Proposed DE-Opt Optimization Framework
2.1 Part I: RS Training with Layer-Wise Granular Hyperparameter Control
2.2 Part II: DE-Based Hyperparameter Auto-Learning
3 Experiments
3.1 General Settings
4 Comparison with Fixed Hyperparameter Models (RQ.1)
4.1 Comparison with Adaptive Hyperparameter Models (RQ.2)
5 Conclusion
References
GELibRec: Third-Party Libraries Recommendation Using Graph Neural Network
1 Introduction
2 Related Work
3 Proposed Approach
3.1 Lib-Based Analysis
3.2 Desc-Based Analysis
3.3 Find Similar Projects
3.4 Aggregator Components
4 Experiment Setup
4.1 Dataset
4.2 Baselines
5 Research and Evaluation
6 Conclusion and Future Work
References
Applications of Machine Learning
Hierarchical Attention Factorization Machine for CTR Prediction
1 Introduction
2 Preliminaries
2.1 Problem Formulation
2.2 Factorization Machines
3 Our Approach
3.1 The HFM Model
3.2 Complexity Analysis
4 Experiments
4.1 Experiment Setup
4.2 Performance Comparison
4.3 Ablation Study of Hierarchical Attention
4.4 Hyper-Parameter Study
5 Conlusion
References
MCRF: Enhancing CTR Prediction Models via Multi-channel Feature Refinement Framework
1 Introduction
2 Related Work
3 The Structure of MCRF
3.1 Embedding Layer
3.2 Gated Feature Refinement Layer
3.3 Multi-channel Feature Refinement Framework
4 Experiments
4.1 Experimental Setup
4.2 Overall Performance Comparison (Q1)
4.3 Compatibility of GFRL and MCRF with Different Models (Q2)
4.4 Effectiveness of GFRL Variants (Q3)
4.5 Superiority of GFRL Compared to Other Structures (Q4)
4.6 The Impact of Channel Numbers of MCRF (Q5)
5 Conclusion
References
CaSS: A Channel-Aware Self-supervised Representation Learning Framework for Multivariate Time Series Classification
1 Introduction
2 Related Work
2.1 Encoders for Time Series Classification
2.2 Pretext Tasks for Time Series
3 The Framework
4 Channel-Aware Transformer
4.1 Embedding Layer
4.2 Co-transformer Layer
4.3 Aggregate Layer
5 Pretext Task
5.1 Next Trend Prediction
5.2 Contextual Similarity
6 Experiments
6.1 Datasets
6.2 Experimental Settings
6.3 Baselines
6.4 Results and Analysis
6.5 Ablation Study
7 Conclusion
References
Temporal Knowledge Graph Entity Alignment via Representation Learning
1 Introduction
2 Related Work
2.1 Temporal Knowledge Graph Embedding
2.2 Embedding-Based Entity Alignment
3 Methodology
3.1 Problem Formulation
3.2 Overview
3.3 GCN-Based TKGs Structure Embedding
3.4 Time-Aware Representation and Joint Entity Representation
3.5 Translation-Based TKGs Attribute Embedding
3.6 Entity Alignment of TKGs
4 Experiments
4.1 Experimental Settings
4.2 Results
4.3 Ablation Study
4.4 Model Analysis
5 Conclusion
References
Similarity-Aware Collaborative Learning for Patient Outcome Prediction
1 Introduction
2 Related Work
3 Problem Formulation
4 Methodology
4.1 Patient Similarity Measurement
4.2 Parallelized LSTM
4.3 Methods for Patient Outcome Prediction
5 Experiments
5.1 Experimental Setup
5.2 Results of Diagnosis Prediction
5.3 Evaluation of Representation Learning
5.4 Results of Mortality Prediction
5.5 Ablation Study
5.6 Study of Weights , and
6 Conclusions
References
Semi-supervised Graph Learning with Few Labeled Nodes
1 Introduction
2 Related Works
3 The Proposed Model
3.1 Preliminary Notations
3.2 Generating Pseudo Labels with GCN
3.3 Generating Pseudo Labels with LPA
3.4 Pseudo Label Generation
3.5 Self-training Process
4 Experiments
4.1 Experiment Setup
4.2 Result Analysis
4.3 Ablation Study
5 Conclusion
References
Human Mobility Identification by Deep Behavior Relevant Location Representation
1 Introduction
2 Related Works
2.1 Trajectory Classification
2.2 Location Representation
3 Methodology
3.1 Preliminary
3.2 Empirical Analysis
3.3 Location Prediction Based Movement Model
3.4 Deep Behavior Relevant Location Representations
3.5 Trajectory-User Linker
4 Experiments
4.1 Datasets
4.2 Baseline Algorithms
4.3 Evaluation Metrics
4.4 Parameter Setup
4.5 Overall Performance (Q1)
4.6 Ablation Experiments (Q2)
4.7 Case Study (Q3)
5 Conclusions
References
Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training
1 Introduction
2 Related Work
3 Federated Grouped Sequential-to-Parallel Learning
3.1 STP: The Sequential-to-Parallel Training Mode
3.2 FedGSP: The Grouped FL Framework to Enable STP
3.3 ICG: The Inter-cluster Grouping Algorithm
4 Experimental Evaluation
4.1 Experiment Setup and Evaluation Metrics
4.2 Results and Discussion
5 Conclusions
References
Transportation-Mode Aware Travel Time Estimation via Meta-learning
1 Introduction
2 Related Work
2.1 Travel Time Estimation
2.2 Meta-learning
3 Problem Formulation
4 Model Architecture
4.1 Base Model
4.2 Meta Optimization
5 Experiments
5.1 Dataset
5.2 Evaluation Metrics and Configuration
5.3 Baselines
5.4 Performance Comparison
6 Conclusion
References
A Deep Reinforcement Learning Based Dynamic Pricing Algorithm in Ride-Hailing
1 Introduction
2 Related Work
3 Basic Settings
3.1 Symbols and Definitions
3.2 Problem Formulation
4 The Algorithm
4.1 Multi-region Dynamic Pricing Algorithm
4.2 Order Matching Algorithm
5 Experimental Analysis
5.1 Benchmark Approaches and Metrics
5.2 Experimental Results
6 Conclusion
References
Peripheral Instance Augmentation for End-to-End Anomaly Detection Using Weighted Adversarial Learning
1 Introduction
2 Related Work
3 Preliminary: Wasserstein GAN with Gradient Penalty (WGAN-GP)
4 Method
4.1 Problem Statement
4.2 Proposed Framework
4.3 Outline of PIA-WAL
5 Experiments
5.1 Datasets and Baselines
5.2 Results and Discussion
6 Application to Merchant Fraud Detection
7 Conclusion
References
HieNet: Bidirectional Hierarchy Framework for Automated ICD Coding
1 Introduction
2 Related Work
3 Proposed Model
3.1 Problem Definition
3.2 Model Architecture
3.3 Document Feature Extractor (DFE)
3.4 Bidirectional Hierarchy Passage Encoder (BHPE)
3.5 Progressive Mechanism (PM) for DP
3.6 Personalized PageRank (PP) for CC
3.7 Aggregation and Training
4 Experimental Setup
4.1 Datasets
4.2 Metrics and Parameter Settings
4.3 Baselines
5 Result and Analysis
5.1 Overall Performance (RQ1)
5.2 Effectiveness of Progressive Mechanism (RQ2)
5.3 Ablation Study (RQ3)
5.4 The Impact of (RQ4)
6 Conclusion and Future Works
References
Efficient Consensus Motif Discovery of All Lengths in Multiple Time Series
1 Introduction
2 Related Work
3 Preliminary Knowledge
3.1 Definitions and Problem Statement
3.2 Fixed-Length Consensus Motif Discovery
4 Proposed Method
4.1 Approach Overview
4.2 Lower Bound Base Information Collection
4.3 Lower Bound Filtering
4.4 Candidate Refinement
4.5 Time Auto-tuning
5 Experiments
5.1 Setup
5.2 Results and Analysis
6 Conclusion
References
LiteWSC: A Lightweight Framework for Web-Scale Spectral Clustering
1 Introduction
2 Related Work
3 The Proposed Method
3.1 Preliminaries
3.2 Framework Overview
3.3 Approximated k-means Based Vector Quantization
3.4 Prototype Laplacian Graph Construction and Eigendecomposition
3.5 Prototype Clustering and Batch Cluster Assignment
4 Framework Analysis
4.1 Perturbation Analysis
4.2 Complexity Analysis
5 Evaluation
5.1 Evaluation on Real Datasets
5.2 Effects of the Parameters
6 Conclusion
References
Dual Confidence Learning Network for Open-World Time Series Classification
1 Introduction
2 Related Work
2.1 Time Series Classification
2.2 Open-World Classification
3 Preliminaries
4 Method
4.1 Temporal Stacked Convolution Encoder
4.2 Time Series Classifier
4.3 Temporal Confidence
4.4 Weibull Distribution Confidence
4.5 Integrator
5 Experiments and Results
5.1 Datasets
5.2 Evaluation Metrics and Implementation Details
5.3 Compared Methods
5.4 Experimental Analysis
5.5 Parameter Sensitivity
6 Conclusions
References
Port Container Throughput Prediction Based on Variational AutoEncoder
1 Introduction
2 Related Works
3 Proposed Approach
3.1 Problem Formulation
3.2 Container Operating Time Forecasting
3.3 Throughput Inference
4 Experimental Setup
4.1 Datasets
4.2 Baseline Approaches
4.3 Experimental Settings
5 Result and Discussion
5.1 Performance Evaluation
5.2 Ablation Study
5.3 Case Study
6 Conclusion
References
Data Source Selection in Federated Learning: A Submodular Optimization Approach
1 Introduction
2 Problem Statement
2.1 Data Source Selection in Federated Learning
2.2 Submodularity Analysis of Data Source Selection
3 Data Source Selection Algorithms
3.1 Static Data Source Selection
3.2 Dynamic Data Source Selection
4 Evaluation
4.1 Experiment Settings
4.2 Experimental Results
5 Conclusion
References
MetisRL: A Reinforcement Learning Approach for Dynamic Routing in Data Center Networks
1 Introduction
2 Related Work
2.1 Routing in Data Centers
2.2 Reinforcement Learning
3 Problem Formulation
4 System Overview and Model Design
4.1 MetisRL: Architecture
4.2 Reinforcement Learning Component
5 Experiment
5.1 Experiment Settings
5.2 Experiment Results
6 Conclusion
References
CLZT: A Contrastive Learning Based Framework for Zero-Shot Text Classification
1 Introduction
2 Related Word
3 Methodology
4 Experiments
4.1 Datasets
4.2 Implementation Details
4.3 Baseline Methods and Evaluation Metrics
4.4 Results and Discussion
4.5 Visualized Analysis
5 Conclusion
References
InDISP: An Interpretable Model for Dynamic Illness Severity Prediction
1 Introduction
2 Related Work
3 Methodology
3.1 Data Preprocessing
3.2 Construction and Embedding of Drug Knowledge Graph
3.3 Temporal Convolutional Network
3.4 The Interpretability of InDISP
4 Conclusions
References
Learning Evolving Concepts with Online Class Posterior Probability
1 Introduction
2 Proposed Method
2.1 Concept Drift Modelling with Class Posterior Probability
2.2 Instance-Sensitive Concept Drift Detection and Learning
3 Experimental Evaluation
3.1 Experiment Setup
3.2 Proof of Concept
3.3 Prediction Performance Analysis
4 Conclusion
References
Robust Dynamic Pricing in Online Markets with Reinforcement Learning
1 Introduction
2 Problem Formulation
3 Robust Dynamic Pricing
3.1 The Framework
3.2 Model Training
3.3 Convergence Analysis
4 Experiments
4.1 Experiment Setting
4.2 Performance Analysis
5 Conclusion
References
Multi-memory Enhanced Separation Network for Indoor Temperature Prediction
1 Introduction
2 Overview
2.1 Problem Definition
3 Methodology
3.1 Source Knowledge Memorization
3.2 Memory-Enhanced Aggregation
4 Experiments
4.1 Settings
4.2 Model Comparison
5 Related Work
6 Conclusion
References
An Interpretable Time Series Classification Approach Based on Feature Clustering
1 Introduction
2 Approach
2.1 Preliminary and Overview
2.2 Feature Definition
2.3 Step One: Feature Generation
2.4 Step Two: Feature Filtering
2.5 Step Three: Feature Clustering
3 Experimental Results
4 Conclusion
References
Generative Adversarial Imitation Learning to Search in Branch-and-Bound Algorithms
1 Introduction
2 Related Work
2.1 Imitation Learning
2.2 Learning the B&B Algorithm
2.3 Learning for Combinatorial Optimization
3 Methodology
3.1 Background
3.2 Neural Network Architecture
3.3 Training
4 Experiments
4.1 Comparative Study
4.2 Learning Curves
5 Conclusion
References
A Trace Ratio Maximization Method for Parameter Free Multiple Kernel Clustering
1 Introduction
2 Design of TRMKC
2.1 Consensus Kernel Learning via CIM
2.2 Clustering on Consensus Kernel
2.3 The Proposed TRMKC
2.4 Optimization
3 Experiments
3.1 Experiment Setup
3.2 Clustering Results Analysis
4 Conclusion
References
Supervised Multi-view Latent Space Learning by Jointly Preserving Similarities Across Views and Samples
1 Introduction
2 Proposed Approach
2.1 View Pair Correlation Preserving
2.2 Label Pair Consistency Preserving
2.3 Complete Objective Function
2.4 Optimization Procedure
3 Experiments
3.1 Experiment Setting
3.2 Results on Synthetic Data and Real-World Datasets
4 Conclusion
References
Market-Aware Dynamic Person-Job Fit with Hierarchical Reinforcement Learning
1 Introduction
2 Related Work
3 Problem Definition
4 The Proposed Approach
5 Experiments
6 Conclusion
References
TEALED: A Multi-Step Workload Forecasting Approach Using Time-Sensitive EMD and Auto LSTM Encoder-Decoder
1 Introduction
2 Related Work and Problem Statement
2.1 Related Work
2.2 Problem Statement
3 System Overview and Model Design
3.1 TEALED Architecture
3.2 Parser
3.3 Forecaster
4 Experimental Analysis
4.1 Evaluation Metrics and Datasets
4.2 Prediction Accuracy Evaluation
5 Conclusion
References
Author Index