Web Information Systems Engineering – WISE 2024 by Barhamgi, Mahmoud; Wang, Hua; Wang, Xin;

Long description:

This five-volume set LNCS 15436 -15440 constitutes the proceedings of the 25th International Conference on Web Information Systems Engineering, WISE 2024, held in Doha, Qatar, in December 2024.

The 110 full papers and 55 short papers were presented in these proceedings were carefully reviewed and selected from 368 submissions. The papers have been organized in the following topical sections as follows:

Part I : Information Retrieval and Text Processing; Text and Sentiment Analysis; Data Analysis and Optimisation; Query Processing and Information Extraction; Knowledge and Data Management.

Part II: Social Media and News Analysis; Graph Machine Learning on Web and Social; Trustworthy Machine Learning; and Graph Data Management.

Part III: Recommendation Systems; Web Systems and Architectures; and Humans and Web Security.

Part IV: Learning and Optimization; Large Language Models and their Applications; and AI Applications.

Part V: Security, Privacy and Trust; Online Safety and Wellbeing through AI; and Web Technologies.a

Table of Contents:

.- Recommendation Systems.

.- Research on micro-videos recommendation method integrating multimodal data and user multi-behavior.

.- Semantic Similarity-Based Graph Contrastive Learning for Recommender System.

.- Feature-Adaptive Meets Domain-Specific Networks for Multi-Domain Recommendation.

.- Next POI Recommendation Based on Time Slot Preferences and Bidirectional Transformation Modeling.

.- DKAF:Diffusion Kolmogorov-Arnold Fourier Hard Sample Mining for CTR.

.- Cross-Domain Sequential Recommendation with Temporal Encoding and Projection-Based Learning.

.- MHHCR: Multi-behavior Heterogeneous Hypergraph Contrastive Recommendation.

.- CSA4Rec: Collaborative Signals Augmentation model based on GCN for Recommendation.

.- Implicit-Relation Knowledge Distillation for Robust Recommendations.

.- Temporal preference and knowledge-aware collaborative attentive network for electrical material recommendation.

.- The Research of Sequence Recommendation Method Based on Heterogeneous Enhanced Transformer with Multi-Behavior Data.

.- MDAP: A Multi-view Disentangled and Adaptive Preference Learning Framework for Cross-Domain Recommendation.

.- Causal Behavior Pattern Inference for News Recommendation through Multi-interest Matching.

.- MIN: Multi-stage Interactive Network for Multimodal Recommendation.

.- RANGER: Context-Aware Service Unit of Work Recommendation for Incremental Scientific Workflow Composition.

.- Optimizing Real Estate Recommendations with Elasticsearch and Machine Learning Techniques.

.- Web Systems and Architectures.

.- A Deep Reinforcement Learning-based Dependent Task Offloading for QoS Optimization in Satellite Edge Computing.

.- HIChain: A Hierarchical IoT Permissioned Blockchain with Edge Cloud Architecture.

.- Leveraging Deep Learning-based Approach for IoT Service Composition through Local Service Selection.

.- An Efficient Device Placement Method for Distributed Training of Multi-branch Neural Network-based Remote Sensing Interpretation.

.- On-Demand Bundling of Cloud and Edge Services.

.- Towards Blockchain like SOA.

.- Clustering-Based Diversity in Service Recommendation.

.- Resource-based Blockchain Integration in Agri-Food Supply Chains.

.- A Scalable Approach for Improving IoT Healthcare Systems with Privacy and Permissioned Blockchain.

.- Acceptance and use of smart home technologies: a focus on Portugal.

.- Humans and Web Security.

.- On How Cialdini's Persuasion Principles Influence Individuals in the Context of Social Engineering: A Qualitative Study.

.- The Impact of Personality Traits and Need for Cognition on Cybersecurity Behavior: A Study Across Arab and European Samples.

.- Reporting Social Media Fraud: Motivations, Barriers, and Reporting Mechanism.

.- Need for Affect and Need for Cognition vs. Cybersecurity Attitude.

.- Detecting ransomware using system calls through transfer learning on a limited feature set.

.- Nob-MIAs: Non-biased Membership Inference Attacks Assessment on Large Language Models with Ex-Post Dataset Construction.