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

<p>In the context of information construction, faced with a large number of network information data, in order to obtain more valuable information in an effective time, researchers put forward a recommendation algorithm for information explosion. Because the recommendation algorithm has been successfully applied in many fields such as business, the relevant algorithm system has also begun to be fully implemented in the field of books. There are many kinds of recommendation algorithms, among which collaborative filtering recommendation technology is the most widely used. Therefore, based on the humanized library service concept, this paper mainly studies the influencing factors of reader satisfaction, and on the basis of constructing multiple linear regression model, clarifies the practical significance of constructing collaborative filtering personalized recommendation system for libraries. Then, an improved clustering algorithm is proposed to reduce the dimensionality of the original matrix, and an empirical analysis is made on the book collaborative filtering personalized recommendation system based on linear regression equation by using the operation idea of calculating the score according to the borrowing time. The final results show that, according to the multiple linear regression model between reader satisfaction and its influencing factors, in order to improve library service quality and build a good learning and reading environment, collaborative filtering algorithm should be combined to build a personalized book recommendation system, and this system is feasible in practical application..</p>
</abstract>

In the context of information construction, faced with a large number of network information data, in order to obtain more valuable information in an effective time, researchers put forward a recommendation algorithm for information explosion. Because the recommendation algorithm has been successfully applied in many fields such as business, the relevant algorithm system has also begun to be fully implemented in the field of books. There are many kinds of recommendation algorithms, among which collaborative filtering recommendation technology is the most widely used. Therefore, based on the humanized library service concept, this paper mainly studies the influencing factors of reader satisfaction, and on the basis of constructing multiple linear regression model, clarifies the practical significance of constructing collaborative filtering personalized recommendation system for libraries. Then, an improved clustering algorithm is proposed to reduce the dimensionality of the original matrix, and an empirical analysis is made on the book collaborative filtering personalized recommendation system based on linear regression equation by using the operation idea of calculating the score according to the borrowing time. The final results show that, according to the multiple linear regression model between reader satisfaction and its influencing factors, in order to improve library service quality and build a good learning and reading environment, collaborative filtering algorithm should be combined to build a personalized book recommendation system, and this system is feasible in practical application..

Realization of Book Collaborative Filtering Personalized Recommendation System Based on Linear Regression Equation

Beijing International Studies University Library

Applied Mathematics and Nonlinear Sciences

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

In the context of information construction, faced with a large number of network information data, in order to obtain more valuable information in an effective...

Realization of Book Collaborative Filtering Personalized Recommendation System Based on Linear Regression Equation

Pubblicato online: 15 lug 2022

Pagine: 831 - 840

Ricevuto: 18 feb 2022

Accettato: 12 apr 2022

DOI: https://doi.org/10.2478/amns.2022.2.0070

Parole chiave
Linear regression, Book lending, Collaborative filtering, Personalized, Recommendation system

© 2023 Yaqi Gao et al., published by Sciendo

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

FIG. 1

FIG. 2

Realization of Book Collaborative Filtering Personalized Recommendation System Based on Linear Regression Equation

Pubblicato online: 15 lug 2022

Pagine: 831 - 840

Ricevuto: 18 feb 2022

Accettato: 12 apr 2022

DOI: https://doi.org/10.2478/amns.2022.2.0070

Parole chiaveLinear regression, Book lending, Collaborative filtering, Personalized, Recommendation system

© 2023 Yaqi Gao et al., published by Sciendo

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

FIG. 1

FIG. 2

Parole chiave
Linear regression, Book lending, Collaborative filtering, Personalized, Recommendation system