Web and Big Data
Description
The five-volume set LNCS 14961, 14962, 14963, 14964 and 14965 constitutes the refereed proceedings of the 8th International Joint Conference on Web and Big Data, APWeb-WAIM 2024, held in Jinhua, China, during August 30-September 1, 2024.
The 171 full papers presented in these proceedings were carefully reviewed and selected from 558 submissions.
The papers are organized in the following topical sections:
Part I: Natural language processing, Generative AI and LLM, Computer Vision and Recommender System.
Part II: Recommender System, Knowledge Graph and Spatial and Temporal Data.
Part III: Spatial and Temporal Data, Graph Neural Network, Graph Mining and Database System and Query Optimization.
Part IV: Database System and Query Optimization, Federated and Privacy-Preserving Learning, Network, Blockchain and Edge computing, Anomaly Detection and Security
Part V: Anomaly Detection and Security, Information Retrieval, Machine Learning, Demonstration Paper and Industry Paper.
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Content
.-
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.