<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>The changing world situation has put the requirement of digital transformation to the traditional exhibition industry. In this paper, we propose a big data-based exhibition exhibit recommendation system, which adopts the improved ALS collaborative filtering algorithm as an offline recommendation and combines window-based real-time TopN recommendation with dynamic perception-based exhibit recommendation as a real-time recommendation algorithm. Then, the big data-based recommendation system is applied to the digital transformation of the exhibition industry to analyze its impact on exhibition-viewing efficiency, exhibition service evaluation, and digital evaluation of exhibition projects. The impact assessment of digitally transformed exhibitions is 0.737 on average, which is 10.61% higher than that of exhibitions in traditional mode. The assessment of the degree of digital application is 0.778 on average, which is 12.59% higher than that of the exhibitions in the traditional mode. The average evaluation of exhibit specialization is 0.872, which is 17.69% higher than that of the traditional model. The research based on big data can provide references and case studies for the digital transformation of traditional exhibition enterprises and help the whole industry chain of the exhibition industry to carry out digital transformation.</p>
</abstract>

The changing world situation has put the requirement of digital transformation to the traditional exhibition industry. In this paper, we propose a big data-based exhibition exhibit recommendation system, which adopts the improved ALS collaborative filtering algorithm as an offline recommendation and combines window-based real-time TopN recommendation with dynamic perception-based exhibit recommendation as a real-time recommendation algorithm. Then, the big data-based recommendation system is applied to the digital transformation of the exhibition industry to analyze its impact on exhibition-viewing efficiency, exhibition service evaluation, and digital evaluation of exhibition projects. The impact assessment of digitally transformed exhibitions is 0.737 on average, which is 10.61% higher than that of exhibitions in traditional mode. The assessment of the degree of digital application is 0.778 on average, which is 12.59% higher than that of the exhibitions in the traditional mode. The average evaluation of exhibit specialization is 0.872, which is 17.69% higher than that of the traditional model. The research based on big data can provide references and case studies for the digital transformation of traditional exhibition enterprises and help the whole industry chain of the exhibition industry to carry out digital transformation.

Research on digital transformation innovation of the exhibition industry in the era of big data

Applied Mathematics and Nonlinear Sciences

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

The changing world situation has put the requirement of digital transformation to the traditional exhibition industry. In this paper, we propose a big...

Research on digital transformation innovation of the exhibition industry in the era of big data

Online veröffentlicht: 18. Aug. 2023

Seitenbereich: -

Eingereicht: 13. Sept. 2022

Akzeptiert: 27. Feb. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00209

Schlüsselwörter
Big data, ALS collaborative filtering, Exhibition industry, Digital transformation, Real-time TopN recommendation.

© 2023 Jieli Zhang, published by Sciendo

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

Research on digital transformation innovation of the exhibition industry in the era of big data

Online veröffentlicht: 18. Aug. 2023

Seitenbereich: -

Eingereicht: 13. Sept. 2022

Akzeptiert: 27. Feb. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00209

SchlüsselwörterBig data, ALS collaborative filtering, Exhibition industry, Digital transformation, Real-time TopN recommendation.

© 2023 Jieli Zhang, published by Sciendo

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

Schlüsselwörter
Big data, ALS collaborative filtering, Exhibition industry, Digital transformation, Real-time TopN recommendation.