Web Information Systems Engineering - WISE 2019

Name: Web Information Systems Engineering - WISE 2019 | 20th International Conference, Hong Kong, China, January 19-22, 2020, Proceedings
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20th International Conference, Hong Kong, China, January 19-22, 2020, Proceedings

Reynold Cheng Nikos Mamoulis Yizhou Sun Xin Huang(Editor)

Springer (Publisher)

Published on 14. November 2019

XVII, 812 pages

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978-3-030-34223-4 (ISBN)

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Content

Intro
Preface
Organization
Contents
BlockChain and Crowdsourcing
GroExpert: A Novel Group-Aware Experts Identification Approach in Crowdsourcing
1 Introduction
2 Related Work
2.1 Upper Bound-Based Approaches
2.2 Lower Bound-Based Approaches
3 Notations and Problem Specification
4 GroExpert Approach
4.1 Phase 1: Pre-process Datasets
4.2 Phase 2: Train the Score Function
4.3 Phase 3: Calculate the Ability Score
5 Experiments
5.1 Experimental Settings
5.2 Performance Comparison
5.3 Training Size
5.4 Sensitivity to Number of Hidden Layers and Number of Hidden Units
6 Conclusions
References
Detecting Fraudulent Accounts on Blockchain: A Supervised Approach
1 Introduction
2 Related Work
3 Methodology
3.1 Data Preparation
3.2 Experiment Setup
3.3 Prediction Models
4 Empirical Results
4.1 Random Forest Results
4.2 Support Vector Machine Results
4.3 XGBoost Results
4.4 Sensitivity Analysis
5 Conclusions and Future Work
References
Locking Mechanism for Concurrency Conflicts on Hyperledger Fabric
1 Introduction
2 Related Work
3 Problem Definition
3.1 The Problem of Concurrency Conflicts in Hyperledger Fabric
3.2 The Instance for Conflicting Transactions
4 The Hyperledger Fabric Architecture
4.1 Nodes in Hyperledger Fabric
4.2 Transaction Flow
5 Proposed Method LMLS
5.1 Locking Mechanism
5.2 Optimization of Ledger Storage
5.3 Steps of LMLS
5.4 Examples for LMLS
6 Experiments and Analysis
6.1 Experiment Setup
6.2 Compared Methods and Metrics for Experiments
6.3 Datasets
6.4 Experiment Results
7 Conclusion
References
Handling Conditional Queries on Hyperledger Fabric Efficiently
1 Introduction
2 Related Work
2.1 Performance Modeling of Blockchain Networks
2.2 Performance Evaluation of Hyperledger Fabric
3 Background
3.1 Data Storage Structure
3.2 Accessing Historical States
4 Problem Statement
5 Proposed Methods
5.1 Baseline Method
5.2 Composite Key Based Method CCK
5.3 AUP Index Based Method AIM
6 Experiment
6.1 Fabric Instance
6.2 System Workload
6.3 Experimental Evaluation
6.4 Time Cost of Baseline
6.5 Time Cost of CCK
6.6 Time Cost of AIM
6.7 Memory Cost of the Three Methos
6.8 Analysis
7 Conclusion and Future Work
References
Machine Learning
Learning to Fuse Multiple Semantic Aspects from Rich Texts for Stock Price Prediction
1 Introduction
2 Related Work
3 Preliminaries
3.1 Definitions and Problem
3.2 Long Short-Term Memory
4 MAFN: Fusing Aspect-Level Textual Information for Stock Price Prediction
4.1 Encoding with Multi-Head Attention
4.2 Decoding with Hierarchical Attention
4.3 Learning and Optimization
5 Experiments
5.1 Experiment Settings
5.2 Comparison Results
5.3 Ablation Tests
5.4 Visualization of Multi-Head Attention Mechanism
6 Conclusion
References
Transfer Learning via Feature Selection Based Nonnegative Matrix Factorization
1 Introduction
2 Related Work
3 Feature Selective Nonnegative Matrix Factorization (FSNMF) Transfer Learning
3.1 Problem Definition
3.2 Proposed Feature Selective Nonnegative Matrix Factorization
4 Experiments and Results
4.1 Results
5 Conclusion
References
Learning Restricted Deterministic Regular Expressions with Counting
1 Introduction
2 Preliminaries
2.1 Regular Expression with Counting
2.2 SORE, ECsore and RCsore
2.3 Descriptivity
2.4 Countable Finite Automaton
3 Inference of RCsores
3.1 Inferring Standard Deterministic Regular Expression: SORE
3.2 Translating SORE to CFA
3.3 Counting with CFA
3.4 Generating RCsore
4 Experiments
4.1 Data and Experiments
4.2 Performance
5 Conclusion
References
Generating Adversarial Examples by Adversarial Networks for Semi-supervised Learning
1 Introduction
2 Related Work
2.1 Adversarial Examples
2.2 Deep Models for Semi-supervised Learning
3 Method
3.1 Problem Definition
3.2 Current Pixel-Value Based Perturbation for SSL
3.3 Our Approach
4 Experiments
4.1 Implementation Details
4.2 Semi-supervised Learning on Synthetic Data
4.3 Semi-supervised and Supervised Learning on MNIST, SVHN, and CIFAR-10
4.4 Visualization of Generated Adversarial Examples
5 Conclusions and Future Work
References
Deep Learning
Dual Path Convolutional Neural Network for Student Performance Prediction
1 Introduction
2 Related Work
3 Framework
3.1 Student Representation
3.2 Dual Path CNN
3.3 Multi-task Learning
3.4 Implementation Details
4 Experiments
4.1 Data Description
4.2 Evaluation Metrics
4.3 Performance Comparison with State-of-the-Art Methods
4.4 Effect of Dual Path Structure
4.5 Contribution of Multi-task Learning
5 Conclusion and Future Work
References
A Case Based Deep Neural Network Interpretability Framework and Its User Study
1 Introduction
2 Related Work
3 Case-Based Interpretability Framework
3.1 The Core Processing Layer
3.2 Web-Based Visualisation and Interactivity
4 Experiments
4.1 Detailed Results
4.2 Post-questionnaire
4.3 General Discussion
5 Conclusion
References
Personalized Book Recommendation Based on a Deep Learning Model and Metadata
1 Introduction
2 Related Work
3 Our Book Recommender System
3.1 The Recurrent Neural Network (RNN) Model
3.2 LCSH
3.3 User Ratings
3.4 User Reviews
3.5 Content Similarity Measure
3.6 Combining Ratings
4 Experimental Results
4.1 Datasets
4.2 Accuracy of Our RNN Classifier
4.3 Evaluation Using Individual Versus Combined Features
4.4 Comparing Book Recommendation Systems
4.5 Human Assessment on Our Recommender
5 Conclusions
References
DINRec: Deep Interest Network Based API Recommendation Approach for Mashup Creation
Abstract
1 Introduction
2 Process of DINRec
2.1 Feature Representation
2.2 Deep Interest Network
3 Experiment
3.1 Dataset Description and Doc2simu Preprocess
3.2 Metrics
3.3 Performance Comparison
3.4 Impact of Training Dataset Sparsity
3.5 Impact of Cosine Similarity Setting
4 Related Works
5 Conclusion and Future Work
Acknowledgment
References
Recommender Systems
Co-purchaser Recommendation Based on Network Embedding
1 Introduction
2 Related Work
2.1 Similarity Search
2.2 Network Embedding
3 Co-purchaser Recommendation
3.1 Formalizations
3.2 Multi-layered Learning Architecture with PathSim
3.3 Co-occurrence Model Based on Truncated Walk
4 Experiments
4.1 Datasets
4.2 Visualization of the Embeddings
4.3 Top-k Purchaser Recommendation
4.4 Co-purchaser Detection
4.5 Parameter Sensitivity
5 Conclusions
References
Community-Based Recommendations on Twitter: Avoiding the Filter Bubble
1 Introduction
2 Related Work
3 Community Analysis
3.1 Twitter Dataset
3.2 Communities' Detection
3.3 The Community Network
4 Filtering Bubble
4.1 Community-Level Approach
4.2 Local Approach
5 CAM - A Community-Aware Model
5.1 Community Profiles
5.2 Community Similarity Score
5.3 Community-Aware Recommendations
5.4 Avoiding the Filter Bubble
6 Experiments
6.1 Settings
6.2 Studying Weights' Impact
6.3 Gains Achieved with the CAM Approach
6.4 Users Activity and Filter Bubbles
7 Conclusion
References
Memory-Augmented Attention Network for Sequential Recommendation
1 Introduction
2 Related Work
2.1 Sequential Recommendation
2.2 Session-Based Recommendation
3 Proposed Method
3.1 Notations and Problem Formulation
3.2 Recommendation Framework: MEANS
3.3 Model Training
4 Experiments
4.1 Experimental Setting
4.2 Impact of Hyper-parameters
4.3 Overall Performance Comparison
4.4 Compares MEANS with SHAN
4.5 Performance over Sessions with Different Length
5 Conclusion
References
Multi-head Attentive Social Recommendation
1 Introduction
2 Related Work
3 Proposed Method
3.1 Notations and Problem Formulation
3.2 Modeling User Feedback
3.3 Multi-head Social Attention
3.4 Modeling Social Structure
3.5 The Unified MAS Model
4 Experiments
4.1 Experimental Settings
4.2 Performance Comparison
4.3 Attention Analysis
5 Conclusion and Future Work
References
CPL: A Combined Framework of Pointwise Prediction and Learning to Rank for top-N Recommendations with Implicit Feedback
1 Introduction
2 Related Work
3 Preliminaries
3.1 Definitions and Notations
3.2 SLIM
3.3 GAPfm
4 Proposed Methodology
4.1 Factorized SLIM (FSLIM)
4.2 GAPfm with Sampling Strategy
4.3 CPLmg Recommendation Model
4.4 Time Complexity
4.5 Recommendation
5 Experimental Results
5.1 Datasets and Settings
5.2 Experimental Comparisons with Previous Models
5.3 Analysis of CPLmg Components
6 Conclusions and Future Work
References
Data Mining
RTIM: A Real-Time Influence Maximization Strategy
1 Introduction
2 IM State of the Art
2.1 Propagation Models
2.2 Properties
2.3 Computing Score
2.4 Algorithms
3 RTIM Approach
3.1 Step I: Pre-processing - Building the Influence Graph
3.2 Step II: User Targeting at Runtime
4 RTIM Model
5 Influence Analysis
6 Experiments
6.1 Experimental Process
6.2 Experimental Results
7 Conclusion
References
LSCMiner: Efficient Low Support Closed Itemsets Mining
1 Introduction
2 Preliminaries
2.1 Problem Definition
2.2 Lattice Traversing and Related Works
2.3 Support Counting on Negative Itemset Tree
3 Closed Itemset on Negative Itemset Tree
3.1 Closed Itemset Determination
3.2 Naïve Method
4 Algorithm: LSCMiner
4.1 Divide-and-Conquer Paradigm
4.2 Pruning
4.3 Complexity
5 Experiments
6 Conclusion
References
Pattern Filtering Attention for Distant Supervised Relation Extraction via Online Clustering
1 Introduction
2 Related Work
3 Methodology
3.1 Instance Feature Extraction
3.2 Positive Patterns Extraction
3.3 Sentence-Level Attention Combined with Pattern Filtering
3.4 Training Objective
4 Experiments
4.1 Dataset and Evaluation Metrics
4.2 Baseline Models
4.3 Experimental Settings
4.4 Experimental Results
4.5 Detailed Analysis
5 Conclusion and Future Works
References
Adaptive Rule Adaptation in Unstructured and Dynamic Environments
1 Introduction
2 Preliminaries and Problem Statement
3 Adaptive Rule Adaptation
3.1 Feature Extraction
3.2 Observation
3.3 Estimation
3.4 Adaptation
4 Gathering Workers Feedback
5 Experiments
5.1 Experiment Settings and Dataset
5.2 Experiment Scenarios
5.3 Results
6 Related Works
6.1 Rule Adaptation
6.2 Multi Armed Bandit Algorithm
7 Conclusion
References
Shadowed Authorization Policies - A Disaster Waiting to Happen?
1 Introduction
2 Background and Related Work
3 Case Study - AWS Policies Management
4 Proposed Approach
5 Event-Calculus Formalism
5.1 Rules Specification
6 Performance Evaluation
7 Conclusion
References
Web-Based Applications
A Dynamic Decision-Making Method Based on Ensemble Methods for Complex Unbalanced Data
Abstract
1 Introduction
2 Related Work
2.1 Difficulties in Learning Complex Unbalanced Data
2.2 Dynamic Ensemble Selection
2.3 Distance-Based Combination Rule
3 Dynamic Ensemble Selection Decision-Making
3.1 Generation of Candidate Classifiers Pool
3.2 Dynamic Selection of Most Appropriate Ensemble
4 Experimental Study
4.1 Analyzing Effectiveness of Dynamic Selection Procedure Proposed for Complex Unbalanced Data
4.2 Comparison of DESD Method with State-of-the-Art Methods
5 Conclusions
Acknowledgments
References
Collaborative Wireframing for Model-Driven Web Engineering
1 Introduction
2 Background and Related Work
3 Agile MDWE with Collaborative Wireframing Support
4 Conceptual Integration
5 Realization
5.1 The Web-Based NRT Collaborative Wireframing Editor
5.2 Transformation Algorithms
6 Evaluation
7 Conclusions and Future Work
References
Handling Disagreement in Ontologies-Based Reasoning via Argumentation
1 Introduction
2 Preliminaries
3 Prudent Argumentation Framework
4 Prudent Argument Labelling
5 Argumentative Inference Relations and Properties
6 Related Work and Discussion
References
A Cost-Efficient Multi-cloud Orchestrator for Benchmarking Containerized Web-Applications
1 Introduction
2 Related Work
3 System Overview
3.1 SDBO Architecture
3.2 SDBO Design
4 Metrics Profiling
4.1 Basic Metrics
4.2 Advanced Metrics
5 Evaluation
5.1 Experiment Setup
5.2 Cost Optimization
5.3 Basic Metrics Profiling
5.4 Advanced Metrics Profiling
5.5 Flexible Execution
6 Conclusion
References
Highlighting Weasel Sentences for Promoting Critical Information Seeking on the Web
1 Introduction
2 Related Works
2.1 Evaluation of Quality and Credibility of Web Information
2.2 Attitude for Critical Information Seeking on the Web
2.3 Promoting Critical Information Seeking
3 Highlighting Weasel Sentences
3.1 Classifier
3.2 Prototype System
3.3 Hypotheses
4 User Study
4.1 Participants
4.2 Tasks
4.3 Design and Procedure
4.4 Experimental System
5 Analysis
5.1 Statistical Approach
5.2 Response Variables
6 Results
6.1 Session Time
6.2 Dwell Time on SERP and Webpage
6.3 Number of Viewed Webpages
6.4 Confidence Change
7 Discussion
7.1 Detection of Weasel Sentences
7.2 Effect of Weasel Sentences During Web Browsing
8 Conclusion
References
Generating an Evolving Skills Network from Job Adverts for High-Demand Skillset Discovery
1 Introduction
2 Related Work
3 The Proposed Framework
3.1 Dataset Compilation
3.2 Graph Construction and Clustering
3.3 Time Series Generation and Evolution
4 Evaluation and Discussion
5 Conclusion
References
Picture News Collection: A Dataset for Automatic Picture News Thumbnail Selection
1 Introduction
2 Related Work
3 Picture News Collection
3.1 Dataset Collecting and Pre-processing
3.2 Dataset Analysis
4 Attention-Based Thumbnail Selection Method
4.1 Two-Pass Attention Encoder (TAE)
4.2 Loss Function
5 Experiments
5.1 Experiment Settings
5.2 Baselines and Methods
5.3 Results and Analyze
6 Conclusion and Future Work
References
A Graph-Based Approach to Explore Relationship Between Hashtags and Images
1 Introduction
2 Image-Hashtag Relationship Verification
3 Quantifying Image-Hashtag Relationship
3.1 Word Embedding Based Approach
3.2 Graph Embedding Based Approach
3.3 Experiments
4 Application
5 Related Work
6 Conclusion and Future Work
References
Entity Linkage and Disambiguation
RDF Graph Anonymization Robust to Data Linkage
1 Introduction
2 Related Work
3 Formal Background
4 Safety Model
5 Safe Anonymization of an RDF Graph
6 Safe Anonymization Robust to :sameAs links
7 Experimental Evaluation
7.1 Runtime Performances
7.2 Evaluation of the Precision Loss
8 Conclusion
References
WebEL: Improving Entity Linking with Extra Web Contexts
1 Introduction
2 Related Work
2.1 Graph-Based Models
2.2 Embedding-Based Models
3 Problem Definition
4 Our Approaches
4.1 WebEL: Embedding-Based Approach
4.1.1 Generating High-Quality Web Contexts
4.1.2 EL with Extra Web Contexts
4.2 WebEL: Graph-Based Approach
5 Experiments
5.1 Dataset and Parameter Setting
5.2 Comparison with Previous Methods
5.3 Experimental Results
6 Conclusions
References
Entity Disambiguation Based on Parse Tree Neighbours on Graph Attention Network
1 Introduction
1.1 Challenges and Contributions
2 Related Work
3 Entity Disambiguation Model
3.1 Feature Extraction
3.2 Neural Network Model
4 Experiments
4.1 Experimental Setting
4.2 Experimental Results
4.3 Component Analysis
5 Conclusion
References
Bibliographic Name Disambiguation with Graph Convolutional Network
1 Introduction
2 Related Work
2.1 Name Disambiguation
2.2 Graph Convolutional Networks
3 Preliminaries
3.1 Problem Formulation
3.2 Graphs in Bibliographic Domain
4 Methodology
4.1 Graph Embedding
4.2 Objective Function
5 Experiments
5.1 Baseline Methods
5.2 Experimental Settings
5.3 Effectiveness Evaluation
5.4 Component Contribution Analysis
6 Conclusion
References
Graph Learning
Semi-supervised Graph Embedding for Multi-label Graph Node Classification
Abstract
1 Introduction
2 Related Work
3 The Proposed Method
3.1 Problem Statement
3.2 Preliminaries: Graph Convolutional Network (GCN)
3.3 ML-GCN: Label Embedding Matrix
3.4 Co-optimization and Negative Sampling
4 Experiments
4.1 Datasets
4.2 Methods in Comparison and Experimental Settings
4.3 Experimental Results
5 Conclusion
Acknowledgments
References
Structural Role Enhanced Attributed Network Embedding
1 Introduction
2 Related Work
2.1 Plain Network Structure Embedding
2.2 Attributed Network Embedding
2.3 Structural Role Proximity Network Embedding
2.4 Our Contribution
3 The Proposed Model
3.1 Problem Definition
3.2 Structural Role Proximity Enhanced Autoencoder
3.3 Neighbor-Modified Skip-Gram Model
3.4 The RolEANE Model Framework
4 Experiments
4.1 Datasets
4.2 Experiment Settings
4.3 Results and Analysis
5 Conclusion
References
Context-Aware Temporal Knowledge Graph Embedding
1 Introduction
2 Related Work
2.1 Traditional KG Embedding
2.2 Temporal KG Embedding
3 Problem Definition
4 Methodology
4.1 Model Overview
4.2 Characterizing of the Context for Temporal Consistency
4.3 Characterizing of the Factual Plausibility
4.4 Objective Function and Training Process
5 Experiments
5.1 Experimental Setting
5.2 Experimental Results
5.3 The Effectiveness of Context Selection
6 Conclusion
References
Interaction Graph Neural Network for News Recommendation
1 Introduction
2 Related Work
2.1 Knowledge Graph Recommendation
2.2 Graph Neural Network
3 Interaction Graph Neural Network
3.1 Knowledge-Aware Convolutional Neural Network
3.2 Embedding Propagation Layer
3.3 Prediction Model
3.4 Optimization
4 Experiment
4.1 Dataset Description
4.2 Experiment Settings
4.3 Performance Comparisons
4.4 Model Analysis and Discussion
5 Conclusion
References
Knowledge Graphs
Gated Relational Graph Neural Network for Semi-supervised Learning on Knowledge Graphs
1 Introduction
2 Preliminaries
3 Related Work
3.1 Representation Learning
3.2 Graph Neural Networks
4 Method
4.1 GRGNN
4.2 Comparison with Related Work
5 Experiments
5.1 Dataset
5.2 Setup
5.3 Results
6 Conclusion
References
Multiple Interaction Attention Model for Open-World Knowledge Graph Completion
1 Introduction
2 Related Work
3 Problem Definition and the Framework
4 The MIA Model
4.1 Input Word Representation
4.2 Multiple Interaction Attention
4.3 Text Context Encoder
4.4 Matching Prediction
5 Experiments
5.1 Datasets
5.2 Experiment Setting
5.3 Open-World Tail Entity Prediction
5.4 Ablation Study
6 Conclusions and Future Work
References
OntoDS: An Ontology-Aware Distributed Storage Scheme for RDF Graphs
1 Introduction
2 RDF over Relational
2.1 The OntoDS Storage Scheme
2.2 Data Insertion
3 Ontology-Aware RDF Graph Distribution
3.1 RDF Ontology Information
3.2 Type Hierarchy Coding
3.3 Queries on Ontologies
4 Experiments
4.1 Datasets
4.2 Experimental Results
5 Related Work
6 Conclusion
A Appendix
A.1 Queries for DB2RDF
A.2 Queries for OntoDS
A.3 Queries for gStoreD
References
Learning Relational Fractals for Deep Knowledge Graph Embedding in Online Social Networks
1 Introduction
2 Related Literature
3 Theories and Methods
3.1 The RFT Architecture
4 Experiments and Results
4.1 Experimental Design
4.2 Experimental Performance
4.3 Testing Results
5 Analysis and Discussion
6 Conclusion
References
Graph Mining
Parameter-Free Structural Diversity Search
1 Introduction
2 Related Work
3 Problem Statement
3.1 Preliminaries
4 Baseline Algorithm
4.1 Core Decomposition
4.2 Computing h(v)
5 Efficient Top-k Search Algorithm
5.1 An Upper Bound of h(v)
5.2 Top-K Structural Diversity Search Framework
5.3 Complexity Analysis
6 Experiments
6.1 Efficiency Evaluation
6.2 Sensitivity Evaluation
6.3 Effectiveness Evaluation
7 Conclusion
References
CoreCube: Core Decomposition in Multilayer Graphs
1 Introduction
2 Problem Definition
3 CoreCube Computation
3.1 Basic CoreCube Algorithm
3.2 Computation-Sharing CoreCube Algorithm
4 CoreCube Storage
5 Experimental Evaluation
5.1 Experimental Setting
5.2 CoreCube Computation
5.3 CoreCube Storage and Query Processing
5.4 Case Study on DBLP
6 Related Work
7 Conclusion
References
Computing Maximum Independent Sets over Large Sparse Graphs
1 Introduction
2 Preliminary
2.1 Reduction Rules
3 Approaches
3.1 MISKernel Algorithm
3.2 MISSolver Algorithm
4 Experiments
4.1 Experimental Results
5 Conclusion
References
Fast Algorithms for Intimate-Core Group Search in Weighted Graphs
1 Introduction
2 Related Work
3 Preliminaries
3.1 Problem Definition
3.2 Existing Intimate-Core Group Search Algorithms
4 Index-Based Local Exploration Algorithms
4.1 K-Core Index
4.2 Solution Overview
4.3 Tree Generation
4.4 Tree-to-Graph Expansion
4.5 Intimate-Core Refinement
5 Experiments
6 Conclusion
References
Text Mining
Unsupervised Ontology- and Sentiment-Aware Review Summarization
1 Introduction
2 Problem Framework
3 Both Problems are NP-Hard
4 Algorithms
4.1 Initialization
4.2 ILP for Optimal Solution
4.3 Randomized Rounding
4.4 Greedy Algorithm
4.5 Adaptation for k-Reviews/Sentences Coverage Problem
5 Experimental Evaluation
5.1 Experiment Setup
5.2 Quantitative Evaluation
5.3 Qualitative Evaluation
6 Related Work
7 Conclusions
References
Enriching the Context: Methods of Improving the Non-contextual Assessment of Sentence Credibility
1 Introduction
2 Related Work
3 Datasets
4 Context Summarizing and Evaluation Methods
4.1 Context Window (CW)
4.2 TF-IDF Keywords + Rule-Based Method of Supplementing the Meaning of Pronouns (TF-IDF + RB)
4.3 TextRank Keywords + Rule-Based Method of Supplementing the Meaning of Pronouns (TextRank + RB)
4.4 Rule-Based Method of Supplementing the Meaning of Pronouns
4.5 Coreference Resolution (COREF)
4.6 Performance Evaluation Measures
5 Results and Discussion
6 Conclusions and Future Work
References
ConceptMap: A Conceptual Approach for Formulating User Preferences in Large Information Spaces
1 Introduction
2 Related Work
3 ConceptMap Interface
4 Solution Overview
5 Experiments
6 Conclusion
References
Helpfulness Prediction for Online Reviews with Explicit Content-Rating Interaction
1 Introduction
2 Related Work
2.1 Automatic Helpfulness Prediction
2.2 Interaction Between Review Content and Star Ratings
3 Explicit Content-Rating Interaction Networks
3.1 Content Encoder
3.2 Rating Enhancer
3.3 Training
4 Experiment Setup
4.1 Datasets
4.2 Baseline Methods
4.3 Hyperparameters
5 Result Analysis
5.1 Comparison with Baseline Methods
5.2 Ablation Studies
5.3 Comparison of Rating Enhancement Methods
6 Conclusion and Future Work
References
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Web Information Systems Engineering - WISE 2019

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