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  • Produktbild: Web Information Systems Engineering – WISE 2024
  • Produktbild: Web Information Systems Engineering – WISE 2024
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Web Information Systems Engineering – WISE 2024 25th International Conference, Doha, Qatar, December 2–5, 2024, Proceedings, Part III

Aus der Reihe Lecture Notes in Computer Science
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72,99 € UVP 87,99 €

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

30.11.2024

Herausgeber

Mahmoud Barhamgi + weitere

Verlag

Springer Singapore

Seitenzahl

458

Maße (L/B/H)

23,5/15,5/2,6 cm

Gewicht

721 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-981-9605-69-9

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

30.11.2024

Herausgeber

Verlag

Springer Singapore

Seitenzahl

458

Maße (L/B/H)

23,5/15,5/2,6 cm

Gewicht

721 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-981-9605-69-9

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: Web Information Systems Engineering – WISE 2024
  • Produktbild: Web Information Systems Engineering – WISE 2024

  • .- 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.