<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>While discussing the importance of office reception work, this article emphasizes the key role of fine management in improving work efficiency and service quality. Against the background of the artificial intelligence era, the article successfully constructs a framework for an office reception work management system by utilizing cutting-edge React and Express technologies. The system not only carries out meticulous module design based on functional requirements, but also proposes a personalized recommendation algorithm integrating clustering and collaborative filtering by optimizing the Similarity of user characteristics and item attributes for joint filtering recommendation to achieve more humanized reception services. To verify the system's effectiveness, this study conducted an application analysis using University Q as a case study. The analysis results show that from 2008 to 2022, the indices of reception preparation, knowledge of reception work, staffing, business skills of reception staff, work attitude and work detail control have been significantly improved, with an increase ranging from 32.0761 to 37.1677, indicating that the management level of office reception work is constantly optimized and shows a rapid growth. This innovative office reception work management system not only realizes refined management, but also comprehensively improves the service efficiency and comprehensive management level, which provides a valuable reference for the management of similar work.</p>
</abstract>

While discussing the importance of office reception work, this article emphasizes the key role of fine management in improving work efficiency and service quality. Against the background of the artificial intelligence era, the article successfully constructs a framework for an office reception work management system by utilizing cutting-edge React and Express technologies. The system not only carries out meticulous module design based on functional requirements, but also proposes a personalized recommendation algorithm integrating clustering and collaborative filtering by optimizing the Similarity of user characteristics and item attributes for joint filtering recommendation to achieve more humanized reception services. To verify the system's effectiveness, this study conducted an application analysis using University Q as a case study. The analysis results show that from 2008 to 2022, the indices of reception preparation, knowledge of reception work, staffing, business skills of reception staff, work attitude and work detail control have been significantly improved, with an increase ranging from 32.0761 to 37.1677, indicating that the management level of office reception work is constantly optimized and shows a rapid growth. This innovative office reception work management system not only realizes refined management, but also comprehensively improves the service efficiency and comprehensive management level, which provides a valuable reference for the management of similar work.

The Practice of Refined Management of Office Reception Work in the Era of Artificial Intelligence

Applied Mathematics and Nonlinear Sciences

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

While discussing the importance of office reception work, this article emphasizes the key role of fine management in improving work efficiency and service...

The Practice of Refined Management of Office Reception Work in the Era of Artificial Intelligence

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 08 gen 2024

Accettato: 17 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0406

Parole chiave
Work management system, Collaborative filtering, K-Means clustering, Personalized recommendation, Office reception

© 2024 Yuefang Zhang, published by Sciendo

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

The Practice of Refined Management of Office Reception Work in the Era of Artificial Intelligence

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 08 gen 2024

Accettato: 17 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0406

Parole chiaveWork management system, Collaborative filtering, K-Means clustering, Personalized recommendation, Office reception

© 2024 Yuefang Zhang, published by Sciendo

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

Parole chiave
Work management system, Collaborative filtering, K-Means clustering, Personalized recommendation, Office reception