Knowledge Science, Engineering and Management

TRAMA

This volume set constitutes the refereed proceedings of the 16th International Conference on Knowledge Science, Engineering and Management, KSEM 2023, which was held in Guangzhou, China, during August 16–18, 2023. The 114 full papers and 30 short papers included in this book were carefully reviewed and selected from 395 submissions. They were organized in topical sections as follows: knowledge science with learning and AI; knowledge engineering research and applications; knowledge management systems; and emerging technologies for knowledge science, engineering and management. 

SOMMARIO

Knowledge Management Systems.- Explainable Multi-type Item Recommendation System based on Knowledge Graph.- A 2D Entity Pair Tagging Scheme for Relation Triplet Extraction.- MVARN: Multi-view attention relation network for figure question answering.- MAGNN-GC: Multi-Head Attentive Graph Neural Networks with Global Context for Session-based Recommendation.- Chinese Relation Extraction with Bi-directional Context-based Lattice LSTM.- MA-TGNN: Multiple Aggregators Graph-Based Model for Text Classification.- Multi-Display Graph Attention Network for Text Classification.- Debiased Contrastive Loss for Collaborative Filtering.- ParaSum: Contrastive Paraphrasing for Low-resource Extractive Text Summarization.- Degree-aware embedding and Interactive feature fusion-based Graph Convolution Collaborative Filtering.- Hypergraph Enhanced Contrastive Learningfor News Recommendation.- Reinforcement Learning-Based Recommendation with User Reviews on Knowledge Graphs.- A Session Recommendation Model based on Heterogeneous Graph Neural Network.- Dialogue State Tracking with a Dialogue-aware Slot-Level Schema Graph Approach.- FedDroidADP: An Adaptive Privacy-Preserving Framework for Federated-Learning-based Android Malware Classification System.- Multi-level and Multi-interest User Interest Modeling for News Recommendation.- CoMeta: Enhancing Meta Embeddings with Collaborative Information in Cold-start Problem of Recommendation.- A Graph Neural Network for Cross-Domain Recommendation Based on Transfer and Inter-Domain Contrastive Learning.- A Hypergraph Augmented and Information Supplementary Network for Session-based Recommendation.- Candidate-aware Attention Enhanced Graph Neural Network for News Recommendation.- Heavy Weighting for Potential Important Clauses.- Knowledge-Aware Two-Stream Decoding for Outline-Conditioned Chinese Story Generation.- Multi-Path based Self-Adaptive Cross-Lingual Summarization.- Temporal Repetition Counting Based on Multi-Stride Collaboration.- Multi-layer Attention Social Recommendation System based on Deep Reinforcement Learning.- SPOAHA: Spark program optimizer based on Artificial Hummingbird Algorithm.- TGKT-based Personalized Learning Path Recommendation with Reinforcement Learning.- Fusion High-Order information with Nonnegative Matrix Factorization Based Community Infomax for Community Detection.- Multi-task learning based skin segmentation.- User Feedback-based Counterfactual Data Augmentation for Sequential Recommendation.- Citation Recommendation Based on Knowledge Graph and Multi-task Learning.- A Pairing Enhancement Approach for AspectSentiment Triplet Extraction.- The Minimal Negated Model Semantics of Assumable Logic Programs.- MT-BICN: Multi-task Balanced Information Cascade Network for Recommendation.

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9783031402883
• Collana: Lecture Notes in Computer Science
• Dimensioni: 235 x 155 mm
• Formato: Brossura
• Illustration Notes: XXIV, 438 p. 120 illus., 115 illus. in color.
• Pagine Arabe: 438
• Pagine Romane: xxiv