<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>Cultural awareness is a context in which the integration and development of traditional arts and crafts can be promoted by establishing an exchange platform. In this paper, the exchange platform is designed through the exploration of traditional handicraft art resources and the study of different classifications. The main points of the platform design and key operational processes are explored based on the analysis of the users’ needs for the traditional handicraft art platform. In response to the lack of accuracy of the traditional buddy recommendation algorithm, a buddy recommendation algorithm incorporating user interests was proposed based on big data to carve out different user preferences for traditional arts and crafts. The user interaction response rate of the platform was good, with TPS of 98.7, 137.4 and 75.6 on the sending side, service side and receiving side, respectively, in stable operation. The friend recommendation algorithm incorporating user interests can effectively dig out user groups with similar interests, which is optimized by 11.83% compared to the traditional friend recommendation algorithm. The exchange platform supported by big data, based on friend recommendations to strengthen user interaction, can effectively promote the exchange and integration of traditional handicraft arts.</p>
</abstract>

Cultural awareness is a context in which the integration and development of traditional arts and crafts can be promoted by establishing an exchange platform. In this paper, the exchange platform is designed through the exploration of traditional handicraft art resources and the study of different classifications. The main points of the platform design and key operational processes are explored based on the analysis of the users’ needs for the traditional handicraft art platform. In response to the lack of accuracy of the traditional buddy recommendation algorithm, a buddy recommendation algorithm incorporating user interests was proposed based on big data to carve out different user preferences for traditional arts and crafts. The user interaction response rate of the platform was good, with TPS of 98.7, 137.4 and 75.6 on the sending side, service side and receiving side, respectively, in stable operation. The friend recommendation algorithm incorporating user interests can effectively dig out user groups with similar interests, which is optimized by 11.83% compared to the traditional friend recommendation algorithm. The exchange platform supported by big data, based on friend recommendations to strengthen user interaction, can effectively promote the exchange and integration of traditional handicraft arts.

The application of friend recommendation algorithm in the design of traditional handicraft art communication platform

Applied Mathematics and Nonlinear Sciences

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

Cultural awareness is a context in which the integration and development of traditional arts and crafts can be promoted by establishing an exchange platform....

The application of friend recommendation algorithm in the design of traditional handicraft art communication platform

Published Online: Sep 25, 2023

Page range: -

Received: Nov 29, 2022

Accepted: Apr 05, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00372

Keywords
Key operational, Big data technology, Traditional handicraft art, Friend recommendation algorithm, User interest.

© 2023 Wenyan Wang et al., published by Sciendo

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

The application of friend recommendation algorithm in the design of traditional handicraft art communication platform

Published Online: Sep 25, 2023

Page range: -

Received: Nov 29, 2022

Accepted: Apr 05, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00372

KeywordsKey operational, Big data technology, Traditional handicraft art, Friend recommendation algorithm, User interest.

© 2023 Wenyan Wang et al., published by Sciendo

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

Keywords
Key operational, Big data technology, Traditional handicraft art, Friend recommendation algorithm, User interest.