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<p>The student community in colleges and universities is based on students’ common living areas, and the community service model in colleges and universities should be student-oriented and centered on students’ development. This paper proposes a one-stop community service model from a student-oriented perspective, with the service community model and service recommendation model being the main component modules. In the service community model, a context-based association relationship mining algorithm is proposed to add time and location contexts to the collaborative filtering algorithm in order to obtain a collection of similar users and services. After constructing the one-stop service community, a service recommendation algorithm based on a trusted coalition is proposed to introduce student credibility and service usage frequency to achieve personalized recommendations of services. University H’s student community implemented the one-stop community service model. After the practice, the mean value of each dimension of the community’s service mode and content evaluation was greater than 3, and the overall satisfaction evaluation value of the community was 39.49, which was extremely significant compared with the evaluation value of University C (P&lt;0.01). The mean value of students’ mental health evaluation reached 3.33.</p>
</abstract>

The student community in colleges and universities is based on students’ common living areas, and the community service model in colleges and universities should be student-oriented and centered on students’ development. This paper proposes a one-stop community service model from a student-oriented perspective, with the service community model and service recommendation model being the main component modules. In the service community model, a context-based association relationship mining algorithm is proposed to add time and location contexts to the collaborative filtering algorithm in order to obtain a collection of similar users and services. After constructing the one-stop service community, a service recommendation algorithm based on a trusted coalition is proposed to introduce student credibility and service usage frequency to achieve personalized recommendations of services. University H’s student community implemented the one-stop community service model. After the practice, the mean value of each dimension of the community’s service mode and content evaluation was greater than 3, and the overall satisfaction evaluation value of the community was 39.49, which was extremely significant compared with the evaluation value of University C (P<0.01). The mean value of students’ mental health evaluation reached 3.33.

Exploring a Student-Centered One-Stop Community Service Model

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Exploring a Student-Centered One-Stop Community Service Model"}

The student community in colleges and universities is based on students’ common living areas, and the community service model in colleges and universities...

Exploring a Student-Centered One-Stop Community Service Model

Publicado en línea: 05 ago 2024

Páginas: -

Recibido: 10 abr 2024

Aceptado: 08 jul 2024

DOI: https://doi.org/10.2478/amns-2024-2041

Palabras clave
Service community model, Service recommendation model, Association relation mining algorithm, Collaborative filtering algorithm, Trusted alliance, One-stop community

© 2024 Jiajia Chu., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Exploring a Student-Centered One-Stop Community Service Model

Publicado en línea: 05 ago 2024

Páginas: -

Recibido: 10 abr 2024

Aceptado: 08 jul 2024

DOI: https://doi.org/10.2478/amns-2024-2041

Palabras claveService community model, Service recommendation model, Association relation mining algorithm, Collaborative filtering algorithm, Trusted alliance, One-stop community

© 2024 Jiajia Chu., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Service community model, Service recommendation model, Association relation mining algorithm, Collaborative filtering algorithm, Trusted alliance, One-stop community