Adegoke Ojeniyi, S. Ajibade, Christiana Kehinde Obafunmiso, Tawakalit Adegbite-Badmus
A recommender system is an information selection system that offers preferences to users and enhances their decision-making. This system is commonly implemented in human-computer-interaction (HCI) intervention because of its information filtering and personalization. However, its success rate in decision-making intervention is considered low and the rationale for this is associated with users’ psychological reactance which is causing unsuccessful recommender system interventions. This paper employs a computational model to depict factors that lead to recommender system rejection by users and how these factors can be enhanced to achieve successful recommender system interventions. The study made use of design science research methodology by executing a computational analysis based on an agent-based simulation approach for the model development and implementation. A total of sixteen model concepts were identified and formalized which were implemented in a Matlab environment using three major case conditions as suggested in previous studies. The result of the study provides an explicit comprehension on interplaying of recommender system that generate psychological reactance which is of great importance to recommender system developers and designers to depict how successful recommender system interventions can be achieved without users experiencing reactance and rejection on the system.
{"title":"Computational Model of Recommender System Intervention","authors":"Adegoke Ojeniyi, S. Ajibade, Christiana Kehinde Obafunmiso, Tawakalit Adegbite-Badmus","doi":"10.1155/2022/3794551","DOIUrl":"https://doi.org/10.1155/2022/3794551","url":null,"abstract":"A recommender system is an information selection system that offers preferences to users and enhances their decision-making. This system is commonly implemented in human-computer-interaction (HCI) intervention because of its information filtering and personalization. However, its success rate in decision-making intervention is considered low and the rationale for this is associated with users’ psychological reactance which is causing unsuccessful recommender system interventions. This paper employs a computational model to depict factors that lead to recommender system rejection by users and how these factors can be enhanced to achieve successful recommender system interventions. The study made use of design science research methodology by executing a computational analysis based on an agent-based simulation approach for the model development and implementation. A total of sixteen model concepts were identified and formalized which were implemented in a Matlab environment using three major case conditions as suggested in previous studies. The result of the study provides an explicit comprehension on interplaying of recommender system that generate psychological reactance which is of great importance to recommender system developers and designers to depict how successful recommender system interventions can be achieved without users experiencing reactance and rejection on the system.","PeriodicalId":8218,"journal":{"name":"Appl. Comput. Intell. Soft Comput.","volume":"87 1","pages":"3794551:1-3794551:13"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90664627","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}
{"title":"Extending machine learning prediction capabilities by explainable AI in financial time series prediction","authors":"T. Çelik, Özgür İcan, E. Bulut","doi":"10.2139/ssrn.4170455","DOIUrl":"https://doi.org/10.2139/ssrn.4170455","url":null,"abstract":"","PeriodicalId":8218,"journal":{"name":"Appl. Comput. Intell. Soft Comput.","volume":"44 1","pages":"109876"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90900566","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}
Li-yang Zhao, Tianqing Chang, J. Zhang, Lei Zhang, Kaixuan Chu, Libin Guo, D. Kong
{"title":"A policy optimization algorithm based on sample adaptive reuse and dual-clipping for robotic action control","authors":"Li-yang Zhao, Tianqing Chang, J. Zhang, Lei Zhang, Kaixuan Chu, Libin Guo, D. Kong","doi":"10.2139/ssrn.4194600","DOIUrl":"https://doi.org/10.2139/ssrn.4194600","url":null,"abstract":"","PeriodicalId":8218,"journal":{"name":"Appl. Comput. Intell. Soft Comput.","volume":"51 1","pages":"109967"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79124682","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}
{"title":"A combined mixed integer programming and deep neural network-assisted heuristics algorithm for the nurse rostering problem","authors":"Ziyi Chen, P. D. Causmaecker, Yajie Dou","doi":"10.2139/ssrn.4020057","DOIUrl":"https://doi.org/10.2139/ssrn.4020057","url":null,"abstract":"","PeriodicalId":8218,"journal":{"name":"Appl. Comput. Intell. Soft Comput.","volume":"9 1","pages":"109919"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73063352","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}
{"title":"Surrogate-assisted hybrid evolutionary algorithm with local estimation of distribution for expensive mixed-variable optimization problems","authors":"Yongcun Liu, Handing Wang","doi":"10.2139/ssrn.4207516","DOIUrl":"https://doi.org/10.2139/ssrn.4207516","url":null,"abstract":"","PeriodicalId":8218,"journal":{"name":"Appl. Comput. Intell. Soft Comput.","volume":"83 ","pages":"109957"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73260025","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}
A. Carvalho, B. Angélico, J. F. Justo, Alexandre Maniçoba de Oliveira, J. I. S. Filho
{"title":"Model reference control by recurrent neural network built with paraconsistent neurons for trajectory tracking of a rotary inverted pendulum","authors":"A. Carvalho, B. Angélico, J. F. Justo, Alexandre Maniçoba de Oliveira, J. I. S. Filho","doi":"10.2139/ssrn.4113620","DOIUrl":"https://doi.org/10.2139/ssrn.4113620","url":null,"abstract":"","PeriodicalId":8218,"journal":{"name":"Appl. Comput. Intell. Soft Comput.","volume":"203 1","pages":"109927"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80267847","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}
Bi-level programming is an efficient tool to tackle decentralized decision-making processes in supply chains with upper level (i.e., leader) and lower level (i.e., follower). The leader makes the first decision while the follower makes the second decision. In this research, a bi-level programming formulation for the problem of location-inventory-routing in a two-echelon supply chain, including a number of central warehouses in the first echelon and retailers in the second echelon with perishable products under uncertain demand, is proposed. The total operational costs at both levels are minimized considering capacity constraints. Due to the uncertain nature of the problem, a scenario-based programming is utilized. The economic condition or unforeseen events such as COVID-19 or Russia-Ukraine war can be good examples for uncertainty sources in today’s world. The model determines the optimal locations of warehouses, the routes between warehouses and retailers, number of received shipments and the amount of inventory held at each retailer. A revised solution method is designed by using multi-choice goal programming for solving the problem. The given revised method attempts to minimize the deviations of each decision maker’s solution from its ideal value assuming that the upper level is satisfied higher than the lower level. Base on some numerical analysis, the proposed solution technique is more sensitive to the upper bounds of the goals rather than the lower bounds.
{"title":"Revised solution technique for a bi-level location-inventory-routing problem under uncertainty of demand and perishability of products","authors":"Fezzeh Partovi, M. Seifbarghy, M. Esmaeili","doi":"10.2139/ssrn.4148557","DOIUrl":"https://doi.org/10.2139/ssrn.4148557","url":null,"abstract":"Bi-level programming is an efficient tool to tackle decentralized decision-making processes in supply chains with upper level (i.e., leader) and lower level (i.e., follower). The leader makes the first decision while the follower makes the second decision. In this research, a bi-level programming formulation for the problem of location-inventory-routing in a two-echelon supply chain, including a number of central warehouses in the first echelon and retailers in the second echelon with perishable products under uncertain demand, is proposed. The total operational costs at both levels are minimized considering capacity constraints. Due to the uncertain nature of the problem, a scenario-based programming is utilized. The economic condition or unforeseen events such as COVID-19 or Russia-Ukraine war can be good examples for uncertainty sources in today’s world. The model determines the optimal locations of warehouses, the routes between warehouses and retailers, number of received shipments and the amount of inventory held at each retailer. A revised solution method is designed by using multi-choice goal programming for solving the problem. The given revised method attempts to minimize the deviations of each decision maker’s solution from its ideal value assuming that the upper level is satisfied higher than the lower level. Base on some numerical analysis, the proposed solution technique is more sensitive to the upper bounds of the goals rather than the lower bounds.","PeriodicalId":8218,"journal":{"name":"Appl. Comput. Intell. Soft Comput.","volume":"143 1","pages":"109899"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76118998","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：