World Wide Web最新文献_第6页

Knowledge-enhanced personalized hierarchical attention network for sequential recommendation 用于顺序推荐的知识增强型个性化分层注意力网络

World Wide Web Pub Date : 2024-01-17 DOI: 10.1007/s11280-024-01236-9

Shuqi Ruan, Chao Yang, Dongsheng Li

{"title":"Knowledge-enhanced personalized hierarchical attention network for sequential recommendation","authors":"Shuqi Ruan, Chao Yang, Dongsheng Li","doi":"10.1007/s11280-024-01236-9","DOIUrl":"https://doi.org/10.1007/s11280-024-01236-9","url":null,"abstract":"Sequential recommendation aims to predict the next items that users will interact with according to the sequential dependencies within historical user interactions. Recently, self-attention based sequence modeling methods have become the mainstream method due to their competitive accuracy. Despite their effectiveness, these methods still have non-trivial limitations: (1) they mainly take the transition patterns between items into consideration but ignore the semantic associations between items, and (2) they mostly focus on dynamic short-term user preferences and fail to consider user static long-term preferences explicitly. To address these limitations, we propose a Knowledge Enhanced Personalized Hierarchical Attention Network (KPHAN), which can incorporate the semantic associations among items by learning from knowledge graphs and capture the fine-grained long- and short-term interests of users through a novel personalized hierarchical attention network. Specifically, we employ the entities and relationships in the knowledge graph to enrich semantic information for items while preserving the structural information of the knowledge graph. The self-attention mechanism then captures semantic associations among items to obtain short-term user preferences more accurately. Finally, a personalized hierarchical attention network is developed to generate the final user preference representations, which can fully capture user static long-term preferences while fusing dynamic short-term preferences. Experimental results on three real-world datasets demonstrate that our method can outperform prior works by 2.7% - 35.5% on HR metrics and 6.7% - 27.9% on NDCG metrics.","PeriodicalId":501180,"journal":{"name":"World Wide Web","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139481608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Privacy-preserving data publishing: an information-driven distributed genetic algorithm 保护隐私的数据发布：信息驱动的分布式遗传算法

World Wide Web Pub Date : 2024-01-15 DOI: 10.1007/s11280-024-01241-y

Yong-Feng Ge, Hua Wang, Jinli Cao, Yanchun Zhang, Xiaohong Jiang

引用次数: 0

Enhancing bitcoin transaction confirmation prediction: a hybrid model combining neural networks and XGBoost 增强比特币交易确认预测：结合神经网络和 XGBoost 的混合模型

World Wide Web Pub Date : 2023-12-26 DOI: 10.1007/s11280-023-01212-9

{"title":"Enhancing bitcoin transaction confirmation prediction: a hybrid model combining neural networks and XGBoost","authors":"","doi":"10.1007/s11280-023-01212-9","DOIUrl":"https://doi.org/10.1007/s11280-023-01212-9","url":null,"abstract":"<h3>Abstract</h3> With Bitcoin being universally recognized as the most popular cryptocurrency, more Bitcoin transactions are expected to be populated to the Bitcoin blockchain system. As a result, many transactions can encounter different confirmation delays. Concerned about this, it becomes vital to help a user understand (if possible) how long it may take for a transaction to be confirmed in the Bitcoin blockchain. In this work, we address the issue of predicting confirmation time within a block interval rather than pinpointing a specific timestamp. After dividing the future into a set of block intervals (i.e., classes), the prediction of a transaction’s confirmation is treated as a classification problem. To solve it, we propose a framework, Hybrid Confirmation Time Estimation Network (Hybrid-CTEN), based on neural networks and XGBoost to predict transaction confirmation time in the Bitcoin blockchain system using three different sources of information: historical transactions in the blockchain, unconfirmed transactions in the mempool, as well as the estimated transaction itself. Finally, experiments on real-world blockchain data demonstrate that, other than XGBoost excelling in the binary classification case (to predict whether a transaction will be confirmed in the next generated block), our proposed framework Hybrid-CTEN outperforms state-of-the-art methods on precision, recall and f1-score on all the multiclass classification cases (4-class, 6-class and 8-class) to predict in which future block interval a transaction will be confirmed.","PeriodicalId":501180,"journal":{"name":"World Wide Web","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139052998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TSEE: a novel knowledge embedding framework for cyberspace security TSEE：用于网络空间安全的新型知识嵌入框架

World Wide Web Pub Date : 2023-12-20 DOI: 10.1007/s11280-023-01220-9

Angxiao Zhao, Zhaoquan Gu, Yan Jia, Wenying Feng, Jianye Yang, Yanchun Zhang

{"title":"TSEE: a novel knowledge embedding framework for cyberspace security","authors":"Angxiao Zhao, Zhaoquan Gu, Yan Jia, Wenying Feng, Jianye Yang, Yanchun Zhang","doi":"10.1007/s11280-023-01220-9","DOIUrl":"https://doi.org/10.1007/s11280-023-01220-9","url":null,"abstract":"Knowledge representation models have been extensively studied and they provide an important foundation for artificial intelligence. However, the existing knowledge representation models or related knowledge embedding methods mostly aim at static or temporal knowledge, which are not suitable for highly spatio-temporal relevant knowledge, such as the cyber security knowledge. In this paper, we propose a knowledge embedding framework called TSEE to handle this problem, which builds on the MDATA model to represent and utilize dynamic knowledge for cyber security. TSEE is composed of knowledge extraction module, knowledge representation module, knowledge embedding module, and situational awareness module. There modules can obtain, transform, and embed cyber security knowledge from different sources, improving the detection capabilities of various complicated attacks. We conduct experiments on the cyber range for evaluation, and the experimental results validate the higher prediction accuracy and stronger extendability than existing embedding methods. The framework can effectively improve the cyber security defense capabilities in the future.","PeriodicalId":501180,"journal":{"name":"World Wide Web","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138819912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Click is not equal to purchase: multi-task reinforcement learning for multi-behavior recommendation 点击不等于购买：针对多种行为推荐的多任务强化学习

World Wide Web Pub Date : 2023-12-20 DOI: 10.1007/s11280-023-01215-6

Huiwang Zhang, Pengpeng Zhao, Xuefeng Xian, Victor S. Sheng, Yongjing Hao, Zhiming Cui

{"title":"Click is not equal to purchase: multi-task reinforcement learning for multi-behavior recommendation","authors":"Huiwang Zhang, Pengpeng Zhao, Xuefeng Xian, Victor S. Sheng, Yongjing Hao, Zhiming Cui","doi":"10.1007/s11280-023-01215-6","DOIUrl":"https://doi.org/10.1007/s11280-023-01215-6","url":null,"abstract":"Reinforcement learning (RL) has achieved ideal performance in recommendation systems (RSs) by taking care of both immediate and future rewards from users. However, the existing RL-based recommendation methods assume that only a single type of interaction behavior (e.g., clicking) exists between user and item, whereas practical recommendation scenarios involve multiple types of user interaction behaviors (e.g., adding to cart, purchasing). In this paper, we propose a Multi-Task Reinforcement Learning model for multi-behavior Recommendation (MTRL4Rec), which gives different actions for users’ different behaviors with a single agent. Specifically, we first introduce a modular network in which modules can be shared or isolated to capture the commonalities and differences across users’ behaviors. Then a task routing network is used to generate routes in the modular network for each behavior task. We adopt a hierarchical reinforcement learning architecture to improve the efficiency of MTRL4Rec. Finally, a training algorithm and a further improved training algorithm are proposed for our model training. Experiments on two public datasets validated the effectiveness of MTRL4Rec.","PeriodicalId":501180,"journal":{"name":"World Wide Web","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138821712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

UaMC: user-augmented conversation recommendation via multi-modal graph learning and context mining UaMC：通过多模态图学习和语境挖掘实现用户增强对话推荐

World Wide Web Pub Date : 2023-12-19 DOI: 10.1007/s11280-023-01219-2

Siqi Fan, Yequan Wang, Xiaobing Pang, Lisi Chen, Peng Han, Shuo Shang

{"title":"UaMC: user-augmented conversation recommendation via multi-modal graph learning and context mining","authors":"Siqi Fan, Yequan Wang, Xiaobing Pang, Lisi Chen, Peng Han, Shuo Shang","doi":"10.1007/s11280-023-01219-2","DOIUrl":"https://doi.org/10.1007/s11280-023-01219-2","url":null,"abstract":"Conversation Recommender System (CRS) engage in multi-turn conversations with users and provide recommendations through responses. As user preferences evolve dynamically during the course of the conversation, it is crucial to understand natural interaction utterances to capture the user’s dynamic preference accurately. Existing research has focused on obtaining user preference at the entity level and natural language level, and bridging the semantic gap through techniques such as knowledge augmentation, semantic fusion, and prompt learning. However, the representation of each level remains under-explored. At the entity level, user preference is typically extracted from Knowledge Graphs, while other modal data is often overlooked. At the natural language level, user representation is obtained from a fixed language model, disregarding the relationships between different contexts. In this paper, we propose User-augmented Conversation Recommendation via Multi-modal graph learning and Context Mining (UaMC) to address above limitations. At the entity level, we enrich user preference by leveraging multi-modal knowledge. At the natural language level, we employ contrast learning to extract user preference from similar contexts. By incorporating the enhanced representation of user preference, we utilize prompt learning techniques to generate responses related to recommended items. We conduct experiments on two public CRS benchmarks, demonstrating the effectiveness of our approach in both the recommendation and conversation subtasks.","PeriodicalId":501180,"journal":{"name":"World Wide Web","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138745557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Community aware graph embedding learning for item recommendation 针对项目推荐的社区感知图嵌入式学习

World Wide Web Pub Date : 2023-12-07 DOI: 10.1007/s11280-023-01224-5

Pengyi Hao, Zhaojie Qian, Shuang Wang, Cong Bai

引用次数: 0

Cross-domain aspect-based sentiment analysis using domain adversarial training 使用领域对抗训练的跨领域基于方面的情感分析

World Wide Web Pub Date : 2023-11-22 DOI: 10.1007/s11280-023-01217-4

Joris Knoester, Flavius Frasincar, Maria Mihaela Truşcǎ

{"title":"Cross-domain aspect-based sentiment analysis using domain adversarial training","authors":"Joris Knoester, Flavius Frasincar, Maria Mihaela Truşcǎ","doi":"10.1007/s11280-023-01217-4","DOIUrl":"https://doi.org/10.1007/s11280-023-01217-4","url":null,"abstract":"Over the last decades, the increasing popularity of the Web came together with an extremely large volume of reviews on products and services useful for both companies and customers to adjust their behaviour with respect to the expressed opinions. Given this growth, Aspect-Based Sentiment Analysis (ABSA) has turned out to be an important tool required to understand people’s preferences. However, despite the large volume of data, the lack of data annotations restricts the supervised ABSA analysis to only a limited number of domains. To tackle this problem a transfer learning strategy is implemented by extending the state-of-the-art LCR-Rot-hop++ model for ABSA with the methodology of Domain Adversarial Training (DAT). The output is a cross-domain deep learning structure, called DAT-LCR-Rot-hop++. The major advantage of DAT-LCR-Rot-hop++ is the fact that it does not require any labeled target domain data. The results are obtained for six different domain combinations with testing accuracies ranging from 35% up until 74%, showing both the limitations and benefits of this approach. Once DAT-LCR-Rot-hop++ is able to find the similarities between domains, it produces good results. However, if the domains are too distant, it is not capable of generating domain-invariant features. This result is amplified by our additional analysis to add the neutral aspects to the positive or negative class. The performance of DAT-LCR-Rot-hop++ is very dependent on the similarity between distributions of source and target domain and the presence of a dominant sentiment class in the training set.","PeriodicalId":501180,"journal":{"name":"World Wide Web","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138536763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Death comes but why: A multi-task memory-fused prediction for accurate and explainable illness severity in ICUs 死亡来了，但为什么:多任务记忆融合预测准确和可解释的重症监护疾病严重程度

World Wide Web Pub Date : 2023-11-16 DOI: 10.1007/s11280-023-01211-w

Weitong Chen, Wei Emma Zhang, Lin Yue

{"title":"Death comes but why: A multi-task memory-fused prediction for accurate and explainable illness severity in ICUs","authors":"Weitong Chen, Wei Emma Zhang, Lin Yue","doi":"10.1007/s11280-023-01211-w","DOIUrl":"https://doi.org/10.1007/s11280-023-01211-w","url":null,"abstract":"Predicting the severity of an illness is crucial in intensive care units (ICUs) if a patient‘s life is to be saved. The existing prediction methods often fail to provide sufficient evidence for time-critical decisions required in dynamic and changing ICU environments. In this research, a new method called MM-RNN (multi-task memory-fused recurrent neural network) was developed to predict the severity of illnesses in intensive care units (ICUs). MM-RNN aims to address this issue by not only predicting illness severity but also generating an evidence-based explanation of how the prediction was made. The architecture of MM-RNN consists of task-specific phased LSTMs and a delta memory network that captures asynchronous feature correlations within and between multiple organ systems. The multi-task nature of MM-RNN allows it to provide an evidence-based explanation of its predictions, along with illness severity scores and a heatmap of the patient’s changing condition. The results of comparison with state-of-the-art methods on real-world clinical data show that MM-RNN delivers more accurate predictions of illness severity with the added benefit of providing evidence-based justifications.","PeriodicalId":501180,"journal":{"name":"World Wide Web","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138536762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intrinsically motivated reinforcement learning based recommendation with counterfactual data augmentation 基于反事实数据增强的内在动机强化学习推荐

World Wide Web Pub Date : 2023-07-15 DOI: 10.1007/s11280-023-01187-7

Xiaocong Chen, Siyu Wang, Lianyong Qi, Yong Li, Lina Yao

{"title":"Intrinsically motivated reinforcement learning based recommendation with counterfactual data augmentation","authors":"Xiaocong Chen, Siyu Wang, Lianyong Qi, Yong Li, Lina Yao","doi":"10.1007/s11280-023-01187-7","DOIUrl":"https://doi.org/10.1007/s11280-023-01187-7","url":null,"abstract":"Deep reinforcement learning (DRL) has shown promising results in modeling dynamic user preferences in RS in recent literature. However, training a DRL agent in the sparse RS environment poses a significant challenge. This is because the agent must balance between exploring informative user-item interaction trajectories and using existing trajectories for policy learning, a known exploration and exploitation trade-off. This trade-off greatly affects the recommendation performance when the environment is sparse. In DRL-based RS, balancing exploration and exploitation is even more challenging as the agent needs to deeply explore informative trajectories and efficiently exploit them in the context of RS. To address this issue, we propose a novel intrinsically motivated reinforcement learning (IMRL) method that enhances the agent’s capability to explore informative interaction trajectories in the sparse environment. We further enrich these trajectories via an adaptive counterfactual augmentation strategy with a customised threshold to improve their efficiency in exploitation. Our approach is evaluated on six offline datasets and three online simulation platforms, demonstrating its superiority over existing state-of-the-art methods. The extensive experiments show that our IMRL method outperforms other methods in terms of recommendation performance in the sparse RS environment.","PeriodicalId":501180,"journal":{"name":"World Wide Web","volume":"41 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138510820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1