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標(biāo)題: Titlebook: Web Information Systems and Applications; 21st International C Cheqing Jin,Shiyu Yang,Yong Zhang Conference proceedings 2024 The Editor(s) [打印本頁]

作者: crusade 時間: 2025-3-21 18:25
書目名稱Web Information Systems and Applications影響因子(影響力)

書目名稱Web Information Systems and Applications影響因子(影響力)學(xué)科排名

書目名稱Web Information Systems and Applications網(wǎng)絡(luò)公開度

書目名稱Web Information Systems and Applications網(wǎng)絡(luò)公開度學(xué)科排名

書目名稱Web Information Systems and Applications被引頻次

書目名稱Web Information Systems and Applications被引頻次學(xué)科排名

書目名稱Web Information Systems and Applications年度引用

書目名稱Web Information Systems and Applications年度引用學(xué)科排名

書目名稱Web Information Systems and Applications讀者反饋

書目名稱Web Information Systems and Applications讀者反饋學(xué)科排名

作者: Gastric 時間: 2025-3-21 21:26

作者: BAIT 時間: 2025-3-22 03:16

作者: Hallmark 時間: 2025-3-22 06:33

作者: 是貪求 時間: 2025-3-22 12:43

作者: superfluous 時間: 2025-3-22 13:30
GTGNN: Global Graph and?Taxonomy Tree for?Graph Neural Network Session-Based Recommendationorporating graph neural networks (GNN) for better performance. However, existing session-based recommendation methods tend to be limited to recommending items that already exist in users’ historical sessions, resulting in inadequate ability to recommend new items that users have never interacted wit

作者: Medley 時間: 2025-3-22 17:15

作者: 跳動 時間: 2025-3-23 00:55

作者: 玩忽職守 時間: 2025-3-23 04:39
Relation-Oriented Temporal Knowledge Graphs Completion Based on Recurrent Neural Network methods learn the nodes on the fixed knowledge graphs by embedding and completing directly, which is computationally complex and difficult to apply concretely. Moreover, most knowledge graphs completion methods ignore the value of temporal information. In this paper, we propose a relational-oriente

作者: BET 時間: 2025-3-23 07:40

作者: LIKEN 時間: 2025-3-23 11:53

作者: PHON 時間: 2025-3-23 15:48
MMPDRec: A Denoising Model for?Knowledge Concepts Recommendation Using Metapathsne learning environments is effective knowledge concept recommendations tailored to the unique preferences and requirements of each student. While existing GCNs-based recommendation systems contribute significantly to the personalization of content, they frequently neglect the intrinsic relationship

作者: 詳細(xì)目錄 時間: 2025-3-23 21:36

作者: 果仁 時間: 2025-3-23 23:43
SPR: A Similar Projection Revisor for?Complex Logical Reasoning over?Knowledge Graphshops reasoning tasks, complex logical reasoning is more related to users need of information retrieval. However, existing complex logical reasoning methods only pay attention to modeling entities, relations or operators, and ignore the assistance of auxiliary information in KGs. To address this issu

作者: 嗎啡 時間: 2025-3-24 03:06

作者: 洞察力 時間: 2025-3-24 10:06
An Generative Entity Relation Extraction Model Based on?UIE for?Legal Text from the problems of error propagation and low efficiency. Although generation based methods provide new solutions, domain-specific design is still necessary. We define ten entity types and three relation types for traffic accident crime cases. And then propose a new generative model UIE-ERNIE-CRF

作者: LUT 時間: 2025-3-24 12:03

作者: 向外才掩飾 時間: 2025-3-24 16:00

作者: legitimate 時間: 2025-3-24 21:18
Uncertain Knowledge Graph Completion with?Rule Miningeach triple to measure its likelihood of being true and make more precisely downstream tasks such as reasoning and decision making possible. Since KGs usually suffer from the problem of incompleteness, methods of rule mining and reasoning for knowledge graph completion are extensively studied due to

作者: 擋泥板 時間: 2025-3-25 00:14

作者: 巨大沒有 時間: 2025-3-25 05:12

作者: Genome 時間: 2025-3-25 09:49
A Study on Context-Matching-Based Joint Training for Chinese Coreference Resolutiond Chinese coreference resolution models, a context-matching-based joint training Chinese coreference resolution model is proposed. The model utilizes RoBERTa(wwm)-large combined with BiLSTM to encode Chinese text, then clusters word embeddings. It uses the results of the word clustering to recognize

作者: Lineage 時間: 2025-3-25 15:42

作者: 神圣不可 時間: 2025-3-25 16:59
DFCDR: Domain-Aware Feature Decoupling and?Fusion for?Cross-Domain Recommendationintroducing domain-specific preferences from the source domain can introduce irrelevant information to the target domain. Furthermore, directly combining domain-general and domain-specific information may hinder the performance of the target domain. In this paper, we propose a domain-aware feature d

作者: Adj異類的 時間: 2025-3-25 21:01
Two-Stage Enhancement for?Recommendation Systems Based on?Contrastive Learninghods often employ graph neural networks to process the relational networks and use contrastive learning to obtain more effective node representations. However, persistent challenges from active users’ noisy data and the cold-start problem related to inactive users impact model performance. Recent st

作者: 假設(shè) 時間: 2025-3-26 03:12

作者: 無辜 時間: 2025-3-26 05:28

作者: abysmal 時間: 2025-3-26 08:30
Popularity-Aware Graph Neural Network with?Global Context for?Session-Based Recommendationsystems model user preferences from the current session using graph neural networks but overlook the varying importance of items with different popularity. To address this, we propose the Popularity-aware Graph Neural Network with Global Context (PGNN-GC), which models popularity features to better

作者: Terminal 時間: 2025-3-26 15:53

作者: 會犯錯誤 時間: 2025-3-26 18:34

作者: 鉗子 時間: 2025-3-27 00:30
Contrastive Learning-Based Cross-Domain Data Augmentation for?Aspect-Based Sentiment Analysisich is rich in labeled data, to the target domain which lacks labeled data. Many recent studies have attempted to address this issue by generating a large amount of labeled target domain data, and the domain adaptive model . has achieved state-of-the-art results. However, training this model require

作者: 勉勵 時間: 2025-3-27 02:48

作者: 平庸的人或物 時間: 2025-3-27 05:45

作者: Mast-Cell 時間: 2025-3-27 09:27

作者: 雪崩 時間: 2025-3-27 17:00

作者: 惡心 時間: 2025-3-27 20:30

作者: Goblet-Cells 時間: 2025-3-28 01:25

作者: Antioxidant 時間: 2025-3-28 02:29
Dual Learning Model of?Code Summary and?Generation Based on?Transformerilizing the probability correlation between CS and CG but also promote alignment between CS and CG models. Based on this, we propose a dual-learning algorithm for CS and CG. Experiments on real Java and Python datasets demonstrated that our model significantly improved the results of CS and CG tasks, surpassing the performance of existing models.

作者: cacophony 時間: 2025-3-28 06:53

作者: 歸功于 時間: 2025-3-28 10:40
Relation-Oriented Temporal Knowledge Graphs Completion Based on Recurrent Neural Networkce of relational information and temporal information. Next, the output value of the cyclic neural network layer is recoded to obtain the final value of the relational representation vector. Finally, our model utilizes the negative sample sampling to predict entities, thus significantly improving the performance of the knowledge graphs completion.

作者: 悠然 時間: 2025-3-28 17:35
SPR: A Similar Projection Revisor for?Complex Logical Reasoning over?Knowledge Graphs in the current query via projection revising module. As a pluggable component, SPR can be embedded in complex logical reasoning baselines to improve their performance without changing baselines structure. Experimental results on two benchmark datasets demonstrate that SPR can increase baselines performance by 1.2% and 1.1% respectively.

作者: 怪物 時間: 2025-3-28 21:48
SPR: A Similar Projection Revisor for?Complex Logical Reasoning over?Knowledge Graphs in the current query via projection revising module. As a pluggable component, SPR can be embedded in complex logical reasoning baselines to improve their performance without changing baselines structure. Experimental results on two benchmark datasets demonstrate that SPR can increase baselines performance by 1.2% and 1.1% respectively.

作者: 比喻好 時間: 2025-3-29 02:21
Enhancing Sentiment Analysis for?Chinese Texts Using a?BERT-Based Model with?a?Custom Attention Mechmore accurate identification and classification of complex emotions. Experimental results on two six-emotion datasets demonstrate superior performance in precision, recall, and F1-score compared to traditional models.

作者: terazosin 時間: 2025-3-29 06:25

作者: MILL 時間: 2025-3-29 08:21
Popularity-Aware Graph Neural Network with?Global Context for?Session-Based Recommendationferences for items of varying popularity. Additionally, we enhance representations using a contrastive learning paradigm. Experiments on three open datasets show that PGNN-GC achieves state-of-the-art performance.

作者: 無可非議 時間: 2025-3-29 14:18

作者: 傀儡 時間: 2025-3-29 16:55
Zhiyuan Wang,Long Shi,Zhen Mei,Xiang Zhao,Zhe Wang,Jun Li

作者: Excitotoxin 時間: 2025-3-29 23:15
Zhenhong Wu,Yuzheng Liu,Xin Shi,Xueqing Zhao,Yun Wang,Guigang Zhang

作者: 銼屑 時間: 2025-3-30 00:18

作者: Harass 時間: 2025-3-30 06:04

作者: 猛烈責(zé)罵 時間: 2025-3-30 09:28
Hua Yin,Shuo Huang,ZhiJian Wang,Yong Ye,WenHui Zhu

作者: 歡樂中國 時間: 2025-3-30 13:50
Yilin Chen,Tianxing Wu,Yunchang Liu,Yuxiang Wang,Guilin Qi

作者: Hectic 時間: 2025-3-30 17:59

作者: MENT 時間: 2025-3-30 21:59

作者: 過度 時間: 2025-3-31 02:28
Iterative Transfer Knowledge Distillation and?Channel Pruning for?Unsupervised Cross-Domain Compress, redundant channels in the student model are pruned to reduce the computational cost while retaining the model accuracy. In particular, the alternation of ACP and TKD ensures effective knowledge transfer, balancing the model size and its performance in the target domain. Experimental results demons

作者: 老人病學(xué) 時間: 2025-3-31 07:16
Iterative Transfer Knowledge Distillation and?Channel Pruning for?Unsupervised Cross-Domain Compress, redundant channels in the student model are pruned to reduce the computational cost while retaining the model accuracy. In particular, the alternation of ACP and TKD ensures effective knowledge transfer, balancing the model size and its performance in the target domain. Experimental results demons

作者: 豪華 時間: 2025-3-31 13:03

作者: Ruptured-Disk 時間: 2025-3-31 14:48
Aspect-Based Sentiment Classification Model Based on Multi-view Information Fusionom different perspectives has not been studied. To solve the above problems, an aspect-based sentiment classification model based on multi-view information fusion is proposed. By constructing an inference result set from the large language model (LLM), the LLM’s results are used to enhance the model

作者: Amendment 時間: 2025-3-31 19:00

作者: BRACE 時間: 2025-3-31 23:49
GTGNN: Global Graph and?Taxonomy Tree for?Graph Neural Network Session-Based Recommendationnomy tree to learn user intent from the perspective of attention mechanism and historical distribution data respectively, simulating the decision-making process when interacting with new items. Meanwhile, to solve the problem that GNN cannot learn new items, zero-shot learning is introduced to infer

作者: paragon 時間: 2025-4-1 02:58

作者: Arboreal 時間: 2025-4-1 09:07

作者: 異常 時間: 2025-4-1 12:09

作者: CUR 時間: 2025-4-1 16:37

作者: 故意釣到白楊 時間: 2025-4-1 20:55
Uncertain Knowledge Graph Completion with?Rule Miningrmer to take rule mining as a sequence-to-sequence task to generate rules. It models the uncertainty in UKGs and infer new triples by differentiable reasoning based on TensorLog with mined rules. The confidence prediction model uses a pre-trained language model to predict the triple confidence given

作者: 莊嚴(yán) 時間: 2025-4-2 00:33