Exploration and analysis of agricultural products e-commerce model based on data mining and 6G internet of things communication

被引：0

作者：

Zhang, Haixia ^{[1
]}

机构：

[1] Harbin Finance Univ, Dept Management, Diantan Rd 65, Harbin 150030, Heilongjiang, Peoples R China

来源：

JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING | 2025年 / 25卷 / 02期

关键词：

data mining; internet of things communication; agricultural products e-commerce; recommendation system; collaborative filtering; RECOMMENDER SYSTEMS; BIG DATA;

D O I：

10.1177/14727978241299558

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The paper focuses on investigating the Collaborative Filtering Recommendation (CFR) algorithm within the recommendation system and the recommendation model of an agricultural product mall. Taking into account the influence of scoring time characteristics, a novel CF algorithm is proposed by combining these characteristics. The CF algorithm model is implemented through the analysis of user data and commodity data. By considering the user's historical purchase information and online behavior records in conjunction with time characteristics, the proposed CF algorithm based on time characteristics aims to enhance the accuracy and efficiency of recommendations. The experimental results show that the proposed model can improve the recommendation quality and efficiency of the agricultural products mall.

引用

页码：1354 / 1367

页数：14

共 32 条

[1] Probabilistic Unsupervised Machine Learning Approach for a Similar Image Recommender System for E-Commerce [J].

Addagarla, Ssvr Kumar ;

Amalanathan, Anthoniraj .

SYMMETRY-BASEL, 2020, 12 (11) :1-17

[2]

Ageed ZS., 2021, Qubahan Academic J, V1, P29, DOI 10.48161/qaj.v1n2a46

[3] A Systematic Study on the Recommender Systems in the E-Commerce [J].

Alamdari, Pegah Malekpour ;

Navimipour, Nima Jafari ;

Hosseinzadeh, Mehdi ;

Safaei, Ali Asghar ;

Darwesh, Aso .

IEEE ACCESS, 2020, 8 :115694-115716

[4] Methodical Aspects of MCDM Based E-Commerce Recommender System [J].

Baczkiewicz, Aleksandra ;

Kizielewicz, Bartlomiej ;

Shekhovtsov, Andrii ;

Watrobski, Jaroslaw ;

Salabun, Wojciech .

JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH, 2021, 16 (06) :2192-2229

[5] A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics [J].

Bock, Frederic E. ;

Aydin, Roland C. ;

Cyron, Christian J. ;

Huber, Norbert ;

Kalidindi, Surya R. ;

Klusemann, Benjamin .

FRONTIERS IN MATERIALS, 2019, 6

[6] Online Recommendation Systems: Factors Influencing Use in E-Commerce [J].

Cabrera-Sanchez, Juan-Pedro ;

Ramos-de-Luna, Iviane ;

Carvajal-Trujillo, Elena ;

Villarejo-Ramos, Angel F. .

SUSTAINABILITY, 2020, 12 (21) :1-15

[7] Personalized recommendation system of e-commerce based on big data analysis [J].

Chen, Hua .

JOURNAL OF INTERDISCIPLINARY MATHEMATICS, 2018, 21 (05) :1243-1247

[8] Dig users' intentions via attention flow network for personalized recommendation [J].

Chen, Yan ;

Dai, Yongfang ;

Han, Xiulong ;

Ge, Yi ;

Yin, Hong ;

Li, Ping .

INFORMATION SCIENCES, 2021, 547 :1122-1135

[9] Personalized Recommendation System Based on Collaborative Filtering for IoT Scenarios [J].

Cui, Zhihua ;

Xu, Xianghua ;

Xue, Fei ;

Cai, Xingjuan ;

Cao, Yang ;

Zhang, Wensheng ;

Chen, Jinjun .

IEEE TRANSACTIONS ON SERVICES COMPUTING, 2020, 13 (04) :685-695

[10] Machine learning and data mining in manufacturing [J].

Dogan, Alican ;

Birant, Derya .

EXPERT SYSTEMS WITH APPLICATIONS, 2021, 166

← 1 2 3 4 →