Pub Date : 2021-03-08DOI: 10.1007/s11518-021-5482-8
Jingtong Zhao
{"title":"Synergy between Customer Segmentation and Personalization","authors":"Jingtong Zhao","doi":"10.1007/s11518-021-5482-8","DOIUrl":"https://doi.org/10.1007/s11518-021-5482-8","url":null,"abstract":"","PeriodicalId":17150,"journal":{"name":"Journal of Systems Science and Systems Engineering","volume":"30 1","pages":"276 - 287"},"PeriodicalIF":1.2,"publicationDate":"2021-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11518-021-5482-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43408919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-06DOI: 10.1007/s11518-021-5481-9
Wenhui Zhou, Xiuzhang Li, Qu Qian
{"title":"Comparison of Gatekeeping and Non-gatekeeping Designs in a Service System with Delay-sensitive Customers","authors":"Wenhui Zhou, Xiuzhang Li, Qu Qian","doi":"10.1007/s11518-021-5481-9","DOIUrl":"https://doi.org/10.1007/s11518-021-5481-9","url":null,"abstract":"","PeriodicalId":17150,"journal":{"name":"Journal of Systems Science and Systems Engineering","volume":"30 1","pages":"125 - 150"},"PeriodicalIF":1.2,"publicationDate":"2021-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11518-021-5481-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"53161664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-29DOI: 10.1007/s11518-021-5477-5
Jianqi Hao, Yongbo Xiao, Shudi Du
{"title":"Physical Examination Data Based Cataract Risk Analysis","authors":"Jianqi Hao, Yongbo Xiao, Shudi Du","doi":"10.1007/s11518-021-5477-5","DOIUrl":"https://doi.org/10.1007/s11518-021-5477-5","url":null,"abstract":"","PeriodicalId":17150,"journal":{"name":"Journal of Systems Science and Systems Engineering","volume":"30 1","pages":"198 - 214"},"PeriodicalIF":1.2,"publicationDate":"2021-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11518-021-5477-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47333390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-05DOI: 10.1007/s11518-020-5475-z
Lei Yang, Caixia Hao, Yijuan Hu
{"title":"Outsourcing or Not? The OEM’s Better Collecting Mode under Cap-and-Trade Regulation","authors":"Lei Yang, Caixia Hao, Yijuan Hu","doi":"10.1007/s11518-020-5475-z","DOIUrl":"https://doi.org/10.1007/s11518-020-5475-z","url":null,"abstract":"","PeriodicalId":17150,"journal":{"name":"Journal of Systems Science and Systems Engineering","volume":"30 1","pages":"151 - 177"},"PeriodicalIF":1.2,"publicationDate":"2021-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11518-020-5475-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"53161524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01Epub Date: 2020-11-17DOI: 10.1007/s11518-020-5470-4
Jiachen Sun, Peter Gloor
Rapid advances in machine learning combined with wide availability of online social media have created considerable research activity in predicting what might be the news of tomorrow based on an analysis of the past. In this work, we present a deep learning forecasting framework which is capable to predict tomorrow's news topics on Twitter and news feeds based on yesterday's content and topic-interaction features. The proposed framework starts by generating topics from words using word embeddings and K-means clustering. Then temporal topic-networks are constructed where two topics are linked if the same user has worked on both topics. Structural and dynamic metrics calculated from networks along with content features and past activity, are used as input of a long short-term memory (LSTM) model, which predicts the number of mentions of a specific topic on the subsequent day. Utilizing dependencies among topics, our experiments on two Twitter datasets and the HuffPost news dataset demonstrate that selecting a topic's historical local neighbors in the topic-network as extra features greatly improves the prediction accuracy and outperforms existing baselines.
{"title":"\"Towards Re-Inventing Psychohistory\": Predicting the Popularity of Tomorrow's News from Yesterday's Twitter and News Feeds.","authors":"Jiachen Sun, Peter Gloor","doi":"10.1007/s11518-020-5470-4","DOIUrl":"https://doi.org/10.1007/s11518-020-5470-4","url":null,"abstract":"<p><p>Rapid advances in machine learning combined with wide availability of online social media have created considerable research activity in predicting what might be the news of tomorrow based on an analysis of the past. In this work, we present a deep learning forecasting framework which is capable to predict tomorrow's news topics on Twitter and news feeds based on yesterday's content and topic-interaction features. The proposed framework starts by generating topics from words using word embeddings and K-means clustering. Then temporal topic-networks are constructed where two topics are linked if the same user has worked on both topics. Structural and dynamic metrics calculated from networks along with content features and past activity, are used as input of a long short-term memory (LSTM) model, which predicts the number of mentions of a specific topic on the subsequent day. Utilizing dependencies among topics, our experiments on two Twitter datasets and the HuffPost news dataset demonstrate that selecting a topic's historical local neighbors in the topic-network as extra features greatly improves the prediction accuracy and outperforms existing baselines.</p>","PeriodicalId":17150,"journal":{"name":"Journal of Systems Science and Systems Engineering","volume":"30 1","pages":"85-104"},"PeriodicalIF":1.2,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11518-020-5470-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38630264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01Epub Date: 2021-05-25DOI: 10.1007/s11518-021-5497-1
Rafik Hadfi, Jawad Haqbeen, Sofia Sahab, Takayuki Ito
Artificial Intelligence is revolutionising our communication practices and the ways in which we interact with each other. This revolution does not only impact how we communicate, but it affects the nature of the partners with whom we communicate. Online discussion platforms now allow humans to communicate with artificial agents in the form of socialbots. Such agents have the potential to moderate online discussions and even manipulate and alter public opinions. In this paper, we propose to study this phenomenon using a constructed large-scale agent platform. At the heart of the platform lies an artificial agent that can moderate online discussions using argumentative messages. We investigate the influence of the agent on the evolution of an online debate involving human participants. The agent will dynamically react to their messages by moderating, supporting, or attacking their stances. We conducted two experiments to evaluate the platform while looking at the effects of the conversational agent. The first experiment is a large-scale discussion with 1076 citizens from Afghanistan discussing urban policy-making in the city of Kabul. The goal of the experiment was to increase the citizen involvement in implementing Sustainable Development Goals. The second experiment is a small-scale debate between a group of 16 students about globalisation and taxation in Myanmar. In the first experiment, we found that the agent improved the responsiveness of the participants and increased the number of identified ideas and issues. In the second experiment, we found that the agent polarised the debate by reinforcing the initial stances of the participant.
{"title":"Argumentative Conversational Agents for Online Discussions.","authors":"Rafik Hadfi, Jawad Haqbeen, Sofia Sahab, Takayuki Ito","doi":"10.1007/s11518-021-5497-1","DOIUrl":"https://doi.org/10.1007/s11518-021-5497-1","url":null,"abstract":"<p><p>Artificial Intelligence is revolutionising our communication practices and the ways in which we interact with each other. This revolution does not only impact how we communicate, but it affects the nature of the partners with whom we communicate. Online discussion platforms now allow humans to communicate with artificial agents in the form of socialbots. Such agents have the potential to moderate online discussions and even manipulate and alter public opinions. In this paper, we propose to study this phenomenon using a constructed large-scale agent platform. At the heart of the platform lies an artificial agent that can moderate online discussions using argumentative messages. We investigate the influence of the agent on the evolution of an online debate involving human participants. The agent will dynamically react to their messages by moderating, supporting, or attacking their stances. We conducted two experiments to evaluate the platform while looking at the effects of the conversational agent. The first experiment is a large-scale discussion with 1076 citizens from Afghanistan discussing urban policy-making in the city of Kabul. The goal of the experiment was to increase the citizen involvement in implementing Sustainable Development Goals. The second experiment is a small-scale debate between a group of 16 students about globalisation and taxation in Myanmar. In the first experiment, we found that the agent improved the responsiveness of the participants and increased the number of identified ideas and issues. In the second experiment, we found that the agent polarised the debate by reinforcing the initial stances of the participant.</p>","PeriodicalId":17150,"journal":{"name":"Journal of Systems Science and Systems Engineering","volume":"30 4","pages":"450-464"},"PeriodicalIF":1.2,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11518-021-5497-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39031823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1007/s11518-020-5474-0
Weixiang Huang, Wenhui Zhou, Feng Luo
{"title":"Coopetition between B2C E-commerce Companies: Price Competition and Logistics Service Cooperation","authors":"Weixiang Huang, Wenhui Zhou, Feng Luo","doi":"10.1007/s11518-020-5474-0","DOIUrl":"https://doi.org/10.1007/s11518-020-5474-0","url":null,"abstract":"","PeriodicalId":17150,"journal":{"name":"Journal of Systems Science and Systems Engineering","volume":"29 1","pages":"730 - 749"},"PeriodicalIF":1.2,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11518-020-5474-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46626848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-26DOI: 10.1007/s11518-020-5471-3
Meng-Tsung Wu, Lijun Ma, Weili Xue
{"title":"Order Timing for Manufacturers with Spot Purchasing Price Uncertainty and Demand Information Updating","authors":"Meng-Tsung Wu, Lijun Ma, Weili Xue","doi":"10.1007/s11518-020-5471-3","DOIUrl":"https://doi.org/10.1007/s11518-020-5471-3","url":null,"abstract":"","PeriodicalId":17150,"journal":{"name":"Journal of Systems Science and Systems Engineering","volume":"29 1","pages":"631 - 654"},"PeriodicalIF":1.2,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11518-020-5471-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46123586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Identifying Influencing Factors for Data Transactions: A Case Study from Shanghai Data Exchange","authors":"Qifeng Tang, Zhiqing Shao, Lihua Huang, Wenyi Yin, Yifan Dou","doi":"10.1007/s11518-020-5473-1","DOIUrl":"https://doi.org/10.1007/s11518-020-5473-1","url":null,"abstract":"","PeriodicalId":17150,"journal":{"name":"Journal of Systems Science and Systems Engineering","volume":"29 1","pages":"697 - 708"},"PeriodicalIF":1.2,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11518-020-5473-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43518414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-26DOI: 10.1007/s11518-020-5472-2
Jae-Dong Hong
{"title":"Application of Integrated Multiple Criteria Data Envelopment Analysis to Humanitarian Logistics Network Design","authors":"Jae-Dong Hong","doi":"10.1007/s11518-020-5472-2","DOIUrl":"https://doi.org/10.1007/s11518-020-5472-2","url":null,"abstract":"","PeriodicalId":17150,"journal":{"name":"Journal of Systems Science and Systems Engineering","volume":"29 1","pages":"709 - 729"},"PeriodicalIF":1.2,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11518-020-5472-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47410622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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