Computational Model of Recommender System Intervention
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 dec...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2022/3794551 |
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| author | Adegoke Ojeniyi Samuel-Soma M. Ajibade Christiana Kehinde Obafunmiso Tawakalit Adegbite-Badmus |
| author_facet | Adegoke Ojeniyi Samuel-Soma M. Ajibade Christiana Kehinde Obafunmiso Tawakalit Adegbite-Badmus |
| author_sort | Adegoke Ojeniyi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a5db2c74fe4f4f6981120d11b70a7a2d |
| institution | Kabale University |
| issn | 1687-9732 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-a5db2c74fe4f4f6981120d11b70a7a2d2025-02-03T05:57:56ZengWileyApplied Computational Intelligence and Soft Computing1687-97322022-01-01202210.1155/2022/3794551Computational Model of Recommender System InterventionAdegoke Ojeniyi0Samuel-Soma M. Ajibade1Christiana Kehinde Obafunmiso2Tawakalit Adegbite-Badmus3Department of Computer ScienceDepartment of Computer EngineeringDepartment of Library and Information ScienceDepartment of Library and Information ScienceA 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.http://dx.doi.org/10.1155/2022/3794551 |
| spellingShingle | Adegoke Ojeniyi Samuel-Soma M. Ajibade Christiana Kehinde Obafunmiso Tawakalit Adegbite-Badmus Computational Model of Recommender System Intervention Applied Computational Intelligence and Soft Computing |
| title | Computational Model of Recommender System Intervention |
| title_full | Computational Model of Recommender System Intervention |
| title_fullStr | Computational Model of Recommender System Intervention |
| title_full_unstemmed | Computational Model of Recommender System Intervention |
| title_short | Computational Model of Recommender System Intervention |
| title_sort | computational model of recommender system intervention |
| url | http://dx.doi.org/10.1155/2022/3794551 |
| work_keys_str_mv | AT adegokeojeniyi computationalmodelofrecommendersystemintervention AT samuelsomamajibade computationalmodelofrecommendersystemintervention AT christianakehindeobafunmiso computationalmodelofrecommendersystemintervention AT tawakalitadegbitebadmus computationalmodelofrecommendersystemintervention |