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

<p>E-commerce, as an emerging value chain model for the global economy, has greatly promoted development, while the impact of digitalization on traditional publishing enterprises is increasingly evident. In this paper, we propose a TextRank keyword extraction algorithm based on comprehensive weights, which extracts and assigns keywords that identify user information, behavior, and product characteristics. We then output a keyword weight table for user information, user behavior, and product keywords. Finally, utilizing an optimized collaborative filtering recommendation algorithm, we establish a recommendation model between the user-commodity matrix to build an e-commerce personalized recommendation system that provides users with more accurate customized recommendations. The practical application of the designed personalized recommendation system is examined to evaluate its accuracy. The MAE of this algorithm is smaller than that of user-based (0.8915, 0.9470) or item-based (0.8873, 0.9327) collaborative filtering algorithms, indicating that the improved collaborative filtering algorithm effectively enhances system recommendation accuracy. The direct effect value of recommendation strength is 0.344, with an indirect effect value of 0.018, leading to the highest overall effect value. This study provides users with convenient and attentive services, significantly enhances user experience quality, and generates substantial profits for the e-commerce platform.</p>
</abstract>

E-commerce, as an emerging value chain model for the global economy, has greatly promoted development, while the impact of digitalization on traditional publishing enterprises is increasingly evident. In this paper, we propose a TextRank keyword extraction algorithm based on comprehensive weights, which extracts and assigns keywords that identify user information, behavior, and product characteristics. We then output a keyword weight table for user information, user behavior, and product keywords. Finally, utilizing an optimized collaborative filtering recommendation algorithm, we establish a recommendation model between the user-commodity matrix to build an e-commerce personalized recommendation system that provides users with more accurate customized recommendations. The practical application of the designed personalized recommendation system is examined to evaluate its accuracy. The MAE of this algorithm is smaller than that of user-based (0.8915, 0.9470) or item-based (0.8873, 0.9327) collaborative filtering algorithms, indicating that the improved collaborative filtering algorithm effectively enhances system recommendation accuracy. The direct effect value of recommendation strength is 0.344, with an indirect effect value of 0.018, leading to the highest overall effect value. This study provides users with convenient and attentive services, significantly enhances user experience quality, and generates substantial profits for the e-commerce platform.

An Accuracy Study of Personalized Recommendation System for E-commerce Based on Big Data Analysis

Applied Mathematics and Nonlinear Sciences

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

E-commerce, as an emerging value chain model for the global economy, has greatly promoted development, while the impact of digitalization on traditional...

An Accuracy Study of Personalized Recommendation System for E-commerce Based on Big Data Analysis

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 28. März 2024

Akzeptiert: 21. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-1923

Schlüsselwörter
SW-TextRank algorithm, Collaborative filtering, Recommender system, e-commerce

© 2024 Hua Zhang, published by Sciendo

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

An Accuracy Study of Personalized Recommendation System for E-commerce Based on Big Data Analysis

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 28. März 2024

Akzeptiert: 21. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-1923

SchlüsselwörterSW-TextRank algorithm, Collaborative filtering, Recommender system, e-commerce

© 2024 Hua Zhang, published by Sciendo

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

Schlüsselwörter
SW-TextRank algorithm, Collaborative filtering, Recommender system, e-commerce