{"product_id":"9783031402883","title":"Knowledge Science, Engineering and Management : 16th International Conference, KSEM 2023, Guangzhou, China, August 16-18, 2023, Proceedings, Part III (Lecture Notes in Computer Science 14119) (1st ed. 2023. 2023. xxiv, 438 S. XXIV, 438 p. 120 illus., 115","description":"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. \nThe 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.  \u003cb\u003eKnowledge Management Systems\u003c\/b\u003e.-\u003cb\u003e \u003c\/b\u003e\u003ci\u003eExplainable Multi-type Item Recommendation System based on Knowledge Graph.- \u003c\/i\u003e\u003ci\u003eA 2D Entity Pair Tagging Scheme for Relation Triplet Extraction.- \u003c\/i\u003e\u003ci\u003eMVARN: Multi-view attention relation network for figure question answering.- \u003c\/i\u003e\u003ci\u003eMAGNN-GC: Multi-Head Attentive Graph Neural Networks with Global Context for Session-based Recommendation.- \u003c\/i\u003e\u003ci\u003eChinese Relation Extraction with Bi-directional Context-based Lattice LSTM.- \u003c\/i\u003e\u003ci\u003eMA-TGNN: Multiple Aggregators Graph-Based Model for Text Classification.- \u003c\/i\u003e\u003ci\u003eMulti-Display Graph Attention Network for Text Classification.- \u003c\/i\u003e\u003ci\u003eDebiased Contrastive Loss for Collaborative Filtering.- \u003c\/i\u003e\u003ci\u003eParaSum: Contrastive Paraphrasing for Low-resource Extractive Text Summarization.- \u003c\/i\u003e\u003ci\u003eDegree-aware embedding and Interactive feature fusion-based Graph Convolution Collaborative Filtering.- \u003c\/i\u003e\u003ci\u003eHypergraph Enhanced Contrastive Learningfor News Recommendation.- \u003c\/i\u003e\u003ci\u003eReinforcement Learning-Based Recommendation with User Reviews on Knowledge Graphs.- \u003c\/i\u003e\u003ci\u003eA Session Recommendation Model based on Heterogeneous Graph Neural Network.- \u003c\/i\u003e\u003ci\u003eDialogue State Tracking with a Dialogue-aware Slot-Level Schema Graph Approach.- \u003c\/i\u003e\u003ci\u003eFedDroidADP: An Adaptive Privacy-Preserving Framework for Federated-Learning-based Android Malware Classification System.- \u003c\/i\u003e\u003ci\u003eMulti-level and Multi-interest User Interest Modeling for News Recommendation.- \u003c\/i\u003e\u003ci\u003eCoMeta: Enhancing Meta Embeddings with Collaborative Information in Cold-start Problem of Recommendation.- \u003c\/i\u003e\u003ci\u003eA Graph Neural Network for Cross-Domain Recommendation Based on Transfer and Inter-Domain Contrastive Learning.- \u003c\/i\u003e\u003ci\u003eA Hypergraph Augmented and Information Supplementary Network for Session-based Recommendation.- \u003c\/i\u003e\u003ci\u003eCandidate-aware Attention Enhanced Graph Neural Network for News Recommendation.- \u003c\/i\u003e\u003ci\u003eHeavy Weighting for Potential Important Clauses.- \u003c\/i\u003e\u003ci\u003eKnowledge-Aware Two-Stream Decoding for Outline-Conditioned Chinese Story Generation.- \u003c\/i\u003e\u003ci\u003eMulti-Path based Self-Adaptive Cross-Lingual Summarization.- \u003c\/i\u003e\u003ci\u003eTemporal Repetition Counting Based on Multi-Stride Collaboration.- \u003c\/i\u003e\u003ci\u003eMulti-layer Attention Social Recommendation System based on Deep Reinforcement Learning.- \u003c\/i\u003e\u003ci\u003eSPOAHA: Spark program optimizer based on Artificial Hummingbird Algorithm.- \u003c\/i\u003e\u003ci\u003eTGKT-based Personalized Learning Path Recommendation with Reinforcement Learning.- \u003c\/i\u003e\u003ci\u003eFusion High-Order information with Nonnegative Matrix Factorization Based Community Infomax for Community Detection.- \u003c\/i\u003e\u003ci\u003eMulti-task learning based skin segmentation.- \u003c\/i\u003e\u003ci\u003eUser Feedback-based Counterfactual Data Augmentation for Sequential Recommendation.- \u003c\/i\u003e\u003ci\u003eCitation Recommendation Based on Knowledge Graph and Multi-task Learning.- \u003c\/i\u003e\u003ci\u003eA Pairing Enhancement Approach for AspectSentiment Triplet Extraction.- \u003c\/i\u003e\u003ci\u003eThe Minimal Negated Model Semantics of Assumable Logic Programs.- \u003c\/i\u003e\u003ci\u003eMT-BICN: Multi-task Balanced Information Cascade Network for Recommendation.\u003c\/i\u003e\u003ci\u003e\u003c\/i\u003e","brand":"SPRINGER, BERLIN; SPRINGER NATURE SWITZERLAND; SPRING","offers":[{"title":"Default Title","offer_id":48848232186091,"sku":"00000_00000_00000_00000","price":164.78,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0758\/4484\/5803\/files\/9783031402883-1.jpg?v=1781717493","url":"https:\/\/kinokuniya.com.sg\/products\/9783031402883","provider":"Books Kinokuniya Singapore","version":"1.0","type":"link"}