Advances in Knowledge Discovery and Data Mining

27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25–28, 2023, Proceedings, Part III

Häftad, Engelska, 2023

Av Hisashi Kashima, Tsuyoshi Ide, Wen-Chih Peng

1 629 kr

Beställningsvara. Skickas inom 10-15 vardagar

Fri frakt för medlemmar vid köp för minst 249 kr.

The 4-volume set LNAI 13935 - 13938 constitutes the proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, which took place in Osaka, Japan during May 25–28, 2023. The 143 papers presented in these proceedings were carefully reviewed and selected from 813 submissions.

Produktinformation

Utgivningsdatum2023-05-27
Mått155 x 235 x 24 mm
Vikt657 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor417
FörlagSpringer International Publishing AG
ISBN9783031333798

Tillhör följande kategorier

Systemvetenskap och AI inom Data och it
Material och digital teknik i undervisningen inom Psykologi och pedagogik
Artificiell intelligens inom Data och it
Databaser inom Data och it
Hårdvara inom Data och it

Big data.- Toward Explainable Recommendation Via Counterfactual Reasoning.- Online Volume Optimization for Notifications via Long Short-Term Value Modeling.- Discovering Geo-referenced Frequent Patterns in Uncertain Geo-referenced Transactional Databases.- Financial data.- Joint Latent Topic Discovery and Expectation Modeling for Financial Markets.- Let the model make financial senses: a Text2Text generative approach for financial complaint identification.- Information retrieval and search.- Web-scale Semantic Product Search With Large Language Models.- Multi-task learning based Keywords weighted Siamese Model for semantic retrieval.- Relation-Aware Network with Attention-Based Loss for Few-Shot Knowledge Graph Completion.- MFBE: Leveraging Multi-Field Information of FAQs for Efficient Dense Retrieval.- Isotropic Representation Can Improve Dense Retrieval.- Knowledge-Enhanced Prototypical Network with Structural Semantics forFew-Shot Relation Classification.- Internet of Things.- MIDFA : Memory-Based Instance Division and Feature Aggregation Network for Video Object Detection.- Medical and biological data.- Vision Transformers for Small Histological Datasets learned through Knowledge Distillation.- Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis.- DKFM: Dual Knowledge-guided Fusion Model for Drug Recommendation.- Hierarchical Graph Neural Network for Patient Treatment Preference Prediction with External Knowledge.- Multimedia and multimodal data.- An Extended Variational Mode Decomposition Algorithm Developed Speech Emotion Recognition Performance.- Dynamically-Scaled Deep Canonical Correlation Analysis.- TCR: Short Video Title Generation and Cover Selection with Attention Refinement.- ItrievalKD: An Iterative Retrieval Framework Assisted with Knowledge Distillation for Noisy Text-to-Image Retrieval.- Recommender systems.- Semantic Relation Transfer for Non-overlapped Cross-domain Recommendations.- Interest Driven Graph Structure Learning for Session-Based Recommendation.- Multi-behavior Guided Temporal Graph Attention Network for Recommendation.- Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for Top-N Recommendation.- Meta-learning Enhanced Next POI Recommendation by Leveraging Check-ins from Auxiliary Cities.- Global-Aware External Attention Deep Model for Sequential Recommendation.- Aggregately Diversified Bundle Recommendation via Popularity Debiasing and Configuration-aware Reranking.- Diversely Regularized Matrix Factorization for Accurate and Aggregately Diversified Recommendation.- kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval.- Staying or Leaving: A Knowledge-Enhanced User Simulator for Reinforcement Learning Based Short Video Recommendation.- RLMixer: A Reinforcement Learning Approach For Integrated Ranking With Contrastive User Preference Modeling.