Formal Analysis of an Agent Support Model for Behaviour Change Intervention

Adegoke, Ojeniyi; Aziz, Azizi Ab; Yusof, Yuhanis

doi:10.18517/ijaseit.6.6.1470

Cited by 5 publications

(3 citation statements)

References 36 publications

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“…In the conceptual model, the arrows represent the causal dependencies of the concepts' interplaying relationship as presented in Table 1. Te concepts formalization is based on previous studies like Adegoke et al [67] and Serrano et al [93]. For instance in equation ( 1), the formalization of the Severity of the Recommendation (Sr) is dependent on Recommendation task (Rt) and Recommendation reject (Rr) as shown in the Conceptual model in Figure 4.…”

Section: Resultsmentioning

confidence: 99%

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Computational Model of Recommender System Intervention

Ojeniyi

Ajibade

Obafunmiso

et al. 2022

Applied Computational Intelligence and Soft Computing

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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.

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Section: Resultsmentioning

confidence: 99%

“…Te second stage is objective identifcation which is referring to the four stated objectives in the research objective section. Te third is the model design and development which will be based on Adegoke et al [67] and Bosse et al [68]. Figure 2 shows the activities in this stage based on the procedure used by Bosse et al [68].…”

Section: Methodsmentioning

confidence: 99%

Computational Model of Recommender System Intervention

Ojeniyi

Ajibade

Obafunmiso

et al. 2022

Applied Computational Intelligence and Soft Computing

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“…The street connectivity parameters used in this study are determined based on the space syntax approach [7]- [10]. For example, visibility is a parameter that connecting user to visualize in an underground metro station [11].…”

Section: Introductionmentioning

confidence: 99%

Differences of Street Connectivity Between Old And New Zones In Malaysian Small Town

Said

Mohamad

2017

International Journal on Advanced Science, Engineering and Information Technology

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From the period before Malaysia's independence until now, there are two significant characteristics of the town formed in small town. Pedestrian behavior in the streets is affected by the connectivity existed from the pattern of the street network of a town. Thus, the existing of characteristics of new and old zone require the pedestrian to travel differently in those environments. By considering a small town consists of the old and new zone and population between 30,000 to 100,000, a street network of Bandar Teluk Intan, Perak was selected. Three sets of data were produced from the land use map and Google Map image, then analyzed it using UCL Depthmap 10. Firstly, solid and void data was used in Visibility graph analysis to measure street connectivity from pedestrian visualization. Second data which convex space data used in Convex space graph analysis to measure street connectivity of spaces in street network that offer pedestrian interaction. Axial-line data is the third data was used in Axial-line graph analysis to measure street connectivity from pedestrian movement in the street network. Finding shows that in the old zone, junctions act as a connector to other streets when pedestrian visualize during travel, space in square and market chose as a meeting place, and the main road as a guide when pedestrian move. In new zone, the main road is a connecting feature for a pedestrian because it's easy to find, connect with most streets, and the possible space to meet people during travel.

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Engineering Responsible And Explainable Models In Human-Agent Collectives

Abeywickrama,

Ramchurn

2023

Applied Artificial Intelligence

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Formal Analysis of an Agent Support Model for Behaviour Change Intervention

Cited by 5 publications

References 36 publications

Computational Model of Recommender System Intervention

Computational Model of Recommender System Intervention

Differences of Street Connectivity Between Old And New Zones In Malaysian Small Town

Engineering Responsible And Explainable Models In Human-Agent Collectives

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