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

<p>Combining the user model and similarity calculation, a personalized recommendation method based on the user preference model is proposed, and the user model is adjusted to update the overall framework of personalized recommendation of user reading preference in response to the user’s operational behaviors such as collecting, commenting, reading, and annotating. We analyze and organize the knowledge service interface structure of the social reading platform and the knowledge service mechanism model based on users’ cognitive, emotional, and social needs and clarify the whole process of satisfying users’ reading needs. According to the age, occupation, and regional characteristics of the users of the social reading platform, as well as the reading interest labels of the users, we explore the influence of different user behaviors on the personalized recommendation results of the social reading platform and the recommendation website, and the more the optimal value of the parameter tends to be 1, the greater the influence of the social reading platform on the recommendation results. The satisfaction index of the social reading platform is 68.50807. The current social reading is not good enough to meet the needs of users. It should be optimized for reading behavior, reading skills, reading knowledge, and reading environment for knowledge service recommendation, respectively.</p>
</abstract>

Combining the user model and similarity calculation, a personalized recommendation method based on the user preference model is proposed, and the user model is adjusted to update the overall framework of personalized recommendation of user reading preference in response to the user’s operational behaviors such as collecting, commenting, reading, and annotating. We analyze and organize the knowledge service interface structure of the social reading platform and the knowledge service mechanism model based on users’ cognitive, emotional, and social needs and clarify the whole process of satisfying users’ reading needs. According to the age, occupation, and regional characteristics of the users of the social reading platform, as well as the reading interest labels of the users, we explore the influence of different user behaviors on the personalized recommendation results of the social reading platform and the recommendation website, and the more the optimal value of the parameter tends to be 1, the greater the influence of the social reading platform on the recommendation results. The satisfaction index of the social reading platform is 68.50807. The current social reading is not good enough to meet the needs of users. It should be optimized for reading behavior, reading skills, reading knowledge, and reading environment for knowledge service recommendation, respectively.

Research on Knowledge Service Mechanism in Social Reading Platform Based on Social Network Recommendation Algorithm

Applied Mathematics and Nonlinear Sciences

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

Combining the user model and similarity calculation, a personalized recommendation method based on the user preference model is proposed, and the user model is...

Research on Knowledge Service Mechanism in Social Reading Platform Based on Social Network Recommendation Algorithm

Published Online: May 22, 2024

Page range: -

Received: Feb 05, 2024

Accepted: Apr 12, 2024

DOI: https://doi.org/10.2478/amns-2024-1168

Keywords
User model, Similarity calculation, Personalized recommendation, Satisfaction index, Social reading platform

© 2024 Dawei Ji, published by Sciendo

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

Research on Knowledge Service Mechanism in Social Reading Platform Based on Social Network Recommendation Algorithm

Published Online: May 22, 2024

Page range: -

Received: Feb 05, 2024

Accepted: Apr 12, 2024

DOI: https://doi.org/10.2478/amns-2024-1168

KeywordsUser model, Similarity calculation, Personalized recommendation, Satisfaction index, Social reading platform

© 2024 Dawei Ji, published by Sciendo

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

Keywords
User model, Similarity calculation, Personalized recommendation, Satisfaction index, Social reading platform