Web and big data Part 2
.- 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..- Con...
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| Weitere Verfasser: | , , , , , |
| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
Singapore
Springer
2024
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| Schriftenreihe: | Lecture notes in computer science
14962 |
| Schlagworte: |
Algorithmen und Datenstrukturen
> Algorithms & data structures
> Angewandte Informatik
> Bildverarbeitung
> COMPUTERS / Computer Science
> COMPUTERS / Data Processing / Optical Data Processing
> COMPUTERS / Database Management / Data Mining
> COMPUTERS / Database Management / General
> COMPUTERS / Expert Systems
> COMPUTERS / Information Theory
> COMPUTERS / Programming / Algorithms
> Data Mining
> Data mining
> Databases
> Datenbanken
> Expert systems / knowledge-based systems
> Image processing
> Information technology: general issues
> Information theory
> Informationstheorie
> Konferenzschrift
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| Online Zugang: | Cover |
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| Zusammenfassung: | .- 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. 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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| Beschreibung: | Literaturangaben |
| Beschreibung: | xviii, 500 Seiten Diagramme |
| ISBN: | 9789819772346 978-981-97-7234-6 |