2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)最新文献_第7页

MENTA: how to balance authorial intention and user agency in virtual environments MENTA:如何在虚拟环境中平衡作者意图和用户代理

2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) Pub Date : 2022-11-01 DOI: 10.1109/WI-IAT55865.2022.00033

D. Lourdeaux, M. Sallak, Rémi Lacaze-Labadie

引用次数: 0

Research on Predicting Food Allergy Based on Recurrent Neural Network 基于递归神经网络的食物过敏预测研究

2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) Pub Date : 2022-11-01 DOI: 10.1109/WI-IAT55865.2022.00139

Qing-yang Dai, Qiang Zhu, Cheng Hong, Shichen Yang

引用次数: 0

Data information prediction based on deep fusion GRU-Stacking 基于深度融合GRU-Stacking的数据信息预测

2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) Pub Date : 2022-11-01 DOI: 10.1109/WI-IAT55865.2022.00140

Quanli Pei, Yulong Chen, Yu Liu, Teng Li, Wei Zhang, Wenrui Xiong, Xinpin Jiang, Min Pan

{"title":"Data information prediction based on deep fusion GRU-Stacking","authors":"Quanli Pei, Yulong Chen, Yu Liu, Teng Li, Wei Zhang, Wenrui Xiong, Xinpin Jiang, Min Pan","doi":"10.1109/WI-IAT55865.2022.00140","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00140","url":null,"abstract":"Machine learning and deep learning are currently widely used to predict data information. The purpose of this paper is to present our contribution to this field by discussing a common event data prediction problem-bicycle sharing demand prediction. The Bicycle Sharing Project has significantly reduced resource consumption and air pollution, but it suffers from the inability of the responsible authorities to predict the demand for bicycles at each station based on spatial and temporal data from the stations, which could result in the use of unnecessary human resources to dynamically balance the number of bicycles at each station. In our work, we propose a GRU-Stacking based model to predict the demand for shared bicycles to solve the above problems. The first level of the prediction model is built using the stacking ensemble learning strategy, the second level is built using the two-layer GRU model, and finally the results of the two levels are fused by weighted average fusion to deeply integrate the two models in order to improve the prediction accuracy of the model. The results demonstrated that our model outperforms popular prediction models like XGBoost, LightGBM, LCE and BiLSTM on three evaluation metrics, R2, RMSLE and MAPE. This shows that our model is well suited to the task of prediction the demand for shared bicycles and can be used to solve other problems involving data information prediction.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124231518","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

Active Learning Strategies Based on Text Informativeness 基于文本信息性的主动学习策略

2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) Pub Date : 2022-11-01 DOI: 10.1109/WI-IAT55865.2022.00015

Ruide Li, Yoko Yamakata, Keishi Tajima

引用次数: 0

Weighted Multi-granulation Containment Neighborhood Rough Set Model 加权多粒包容邻域粗糙集模型

2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) Pub Date : 2022-11-01 DOI: 10.1109/WI-IAT55865.2022.00134

Zhiqiang Wang, Tingting Zheng, Qing Li, Xin Sun

{"title":"Weighted Multi-granulation Containment Neighborhood Rough Set Model","authors":"Zhiqiang Wang, Tingting Zheng, Qing Li, Xin Sun","doi":"10.1109/WI-IAT55865.2022.00134","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00134","url":null,"abstract":"Neighborhood rough set theory is an important method for dealing with uncertainty, fuzziness and undefined objects, and neighborhood rough set theory based on binary relation is studied by scholars a lot. However, most of the current research is used to deal with equivalent binary relations or other binary relations with stringent condition, which has been unable to meet the rapid development of data. This paper proposes a new multi-granulation rough set that can deal with general binary relations based on the Cj neighborhoods, and verifies its related properties. At the same −time, it has richer meanings through weighted methods. Theoretical analysis and practical examples show that this rough set has better ability to process data. Finally, a weighted attribute reduction algorithm is designed based on the significant function under the general binary relation. The experimental results show that this rough set can handle complex data well. The research in this paper develops the theory of classical rough sets, and provides a theoretical basis for the knowledge acquisition of information systems under the general binary relation.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126618872","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

Predict Turnaround Time of Hospital Discharge 预测出院周转时间

2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) Pub Date : 2022-11-01 DOI: 10.1109/WI-IAT55865.2022.00067

Bin Jia, Jing Zhang, X. Jia

引用次数: 0

Cascade-based Adversarial Optimization for Influence Prediction 基于级联的影响预测对抗优化

2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) Pub Date : 2022-11-01 DOI: 10.1109/WI-IAT55865.2022.00133

Bei Yuan, Meiling Li, Jie Chen, Shu Zhao, Yanping Zhang, Fulan Qian

{"title":"Cascade-based Adversarial Optimization for Influence Prediction","authors":"Bei Yuan, Meiling Li, Jie Chen, Shu Zhao, Yanping Zhang, Fulan Qian","doi":"10.1109/WI-IAT55865.2022.00133","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00133","url":null,"abstract":"Influence prediction methods based on specific diffusion models are not suitable for information spread in real social networks. In addition, influence prediction methods based on information such as structure graphs and text content are difficult to promote due to limitations in information acquisition. Aiming at the two problems faced in the research of influence prediction, the sequential and non-sequential dependencies in the information diffusion process are captured through the information cascades time-series information, and the Multi-Dependency Diffusion Attention Neural (MDDAN) network is proposed. The sequential dependency and non-sequential dependency of cascades are obtained through recurrent neural networks and attention mechanisms, respectively. At the same time, the model captures the user’s dynamic preferences through the attention mechanism because the information has a time decay characteristic. To reduce the noise interference in the information diffusion, a Cascade-based Adversarial Optimization (CAO) strategy is proposed. To prove that this strategy effectively enhances the generalization ability of cascade-based influence prediction models, we apply it to MDDAN and propose an Adversarial Multi-Dependency Diffusion Attention Neural (AMDDAN) network. Experiments on three real social network datasets show that MDDAN outperforms state-of-the-art cascade prediction models, and the addition of adversarial perturbation to AMDDAN improves the robustness of MDDAN.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114769203","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

Improving Recommendation Diversity by Maximizing Semantic Volume 最大化语义量提高推荐多样性

2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) Pub Date : 2022-11-01 DOI: 10.1109/WI-IAT55865.2022.00079

Atom Sonoda, F. Toriumi, Hiroto Nakajima

引用次数: 0

SparseMult: A Tensor Decomposition model based on Sparse Relation Matrix SparseMult:基于稀疏关系矩阵的张量分解模型

2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) Pub Date : 2022-11-01 DOI: 10.1109/WI-IAT55865.2022.00124

Zhiwen Xie, Runjie Zhu, Meng Zhang, Jin Liu

{"title":"SparseMult: A Tensor Decomposition model based on Sparse Relation Matrix","authors":"Zhiwen Xie, Runjie Zhu, Meng Zhang, Jin Liu","doi":"10.1109/WI-IAT55865.2022.00124","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00124","url":null,"abstract":"Knowledge graphs (KGs) provide rich knowledge for lots of downstream tasks, such as recommendation system and question answering. However, KGs suffer from an incompleteness issue, namely, lots of relations between entities are missing. Link prediction, also known as knowledge graph completion (KGC), aims to predict missing relationships between entities. The models based on tensor decomposition, such as Rescal and DistMult, are one of the most effective methods to solve the link prediction task. However, previous Rescal method lack the ability to scale to large KGs due to the large amount of parameters. DistMult simplifies Rescal using diagonal matrices to represent relations, while it suffers from the limitation of dealing with antisymmetric relations. To address these problems, in this paper, we propose a novel tensor decomposition model based on sparse relation matrix, which is named as SparseMult. We conduct extensive experiments on link prediction task and experimental results show that our SparseMult model outperforms most of the state-of-the-art methods.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124398150","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

Retrieving and Ranking Relevant Products from Boolean Natural Language Queries 从布尔自然语言查询中检索和排序相关产品

2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) Pub Date : 2022-11-01 DOI: 10.1109/WI-IAT55865.2022.00051

Matthew Moulton, Siqi Gao, Yiu-Kai Ng

{"title":"Retrieving and Ranking Relevant Products from Boolean Natural Language Queries","authors":"Matthew Moulton, Siqi Gao, Yiu-Kai Ng","doi":"10.1109/WI-IAT55865.2022.00051","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00051","url":null,"abstract":"E-commerce is a massive sector in the US economy, generating $767.7 billion in revenue in 2021. E-commerce sites maximize their revenue by helping customers find, examine, and purchase products. To help users easily find the most relevant products in the database for their individual needs, e-commerce sites are equipped with a product retrieval system. Many of these systems parse user-specified constraints or keywords embedded in a simple natural language query, which is generally easier and faster for the customer to specify their needs than navigating a product specification form, and does not require the seller to design or develop such form. These natural language retrieval systems, however, suffer from low relevance in retrieved products, especially for complex constraints specified on products. The reduced accuracy is in part due to under-utilizing the rich semantics of natural language, specifically queries that include Boolean operators, and lacking of the ranking on partially- matched relevant results that could be of interested to the customers. This undesirable effect costs e-commerce vendors to lose sells on their merchandise. In solving this problem, we propose a product retrieval system, called QuePR. The advantages of QuePR are its ability to process explicit and implicit Boolean operators in queries, handle natural language queries using similarity measures on partially-matched records, and perform best guess or match on ambiguous or incomplete queries. QuePR is unique, easy to use, and scalable to all product categories. We have conducted different performance analysis to verify the effectiveness of QuePR and compared QuePR with other ranking and retrieval systems. The empirical results show that QuePR outperforms others and is efficient.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121875918","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