Cross-Border E-Commerce Intelligent Information Recommendation System Based on Deep Learning

Li, Liuqing

doi:10.1155/2022/6602471

Cited by 8 publications

(4 citation statements)

References 17 publications

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“…In the scale of CBEC transactions, the proportion also far exceeds that of imported CBEC. With the rapid development of market size and profitability, the times also have new requirements for all aspects of China's e-commerce industry [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

The Development Relationship between Cross-Border e-Commerce and Internet of Things Technology Coupling in Digital Economy Based on Neural Network Model

Liu¹

2022

Computational and Mathematical Methods in Medicine

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Compared with the traditional trading model, the problem of information asymmetry in export cross-border e-commerce (CBEC) transactions actually exists. Since the buyers and sellers of export CBEC are located in different countries, it is difficult for both parties to make accurate judgments on each other’s credit. Besides, the number of export CBEC financing is relatively small, and the financing situation is not optimistic. Fortunately, the Internet of Things (IOT) technology has a wide range of applications and strong social penetration, especially in e-commerce, which can effectively improve payment, logistics, and distribution. By employing the concept of the IOT, this paper expounds the application of the IOT in the e-commerce transaction process and the impact of the IOT on the development of e-commerce. Besides, this paper also proposes some security issues in the application of the IOT in e-commerce. In addition, this paper analyzes the relationship and development trend of CBEC and IOT technology; meanwhile, it also points out the practical problems currently faced by combining the regional advantage of a certain region. The e-commerce industry develops IOT technology and drives innovation-driven development in the region. In addition, this paper builds an evaluation index system for the development of export CBEC enterprises based on the analysis of the problems and reasons by using the sample data to train the BP neural network and using the test samples to test the model. It is found that the model accuracy rate is 89.47%. Then, this paper also takes the export CBEC company A as an example to conduct empirical research and to prove the operability of the credit evaluation model. Finally, it provides relevant suggestions for improving the credit evaluation system of export CBEC enterprises.

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Section: Introductionmentioning

confidence: 99%

The Development Relationship between Cross-Border e-Commerce and Internet of Things Technology Coupling in Digital Economy Based on Neural Network Model

Liu¹

2022

Computational and Mathematical Methods in Medicine

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“…With the popularity and growth of the Internet, the development of online content and services has led to heavy information overload, which has in turn hampered the quick access to items of interest for users [1]. In certain industrial sectors (e-commerce, music, news), accurate recommendations to users can generally facilitate considerable economic benefits, which can further drive the research and development of recommendation algorithms [2,3]. Benefiting from the accumulation of user behavior data, sequential recommendations have become a mainstream approach to improving click-through-rate (CTR) prediction by explicitly modeling users' historical behavior of users to represent their personalized preferences [4].…”

Section: Introductionmentioning

confidence: 99%

Knowledge Graph-aware Deep Interest Extraction Network on Sequential Recommendation

Wang

et al. 2023

Preprint

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Sequential recommendation is the mainstream approach in the field of click-through-rate (CTR) prediction for modeling users' behavior. This behavior implies the change of the user's interest, and the goal of sequential recommendation is to capture this dynamic change. However, existing studies have focused on designing complex dedicated networks to capture user interests from user behavior sequences, while neglecting the use of auxiliary information. Recently, knowledge graph (KG) has gradually attracted the attention of researchers as a structured auxiliary information. Items and their attributes in the recommendation, can be mapped to knowledge triples in the KG. Therefore, the introduction of KG to recommendation can help us obtain more expressive item representations. Since KG can be considered a special type of graph, it is possible to use the graph neural network (GNN) to propagate the rich information contained in the KG into the item representation. Based on this idea, this paper proposes a recommendation method that uses KG as auxiliary information. The method first propagates the knowledge information in the KG using GNN to obtain a knowledge-rich item representation. Then the temporal features in the item sequence are extracted using a transformer for CTR prediction, namely the Knowledge Graph-aware Deep Interest Extraction network (KGDIE). To evaluate the performance of this model, we conducted extensive experiments on two real datasets with different scenarios. The results showed that the KGDIE method could outperform several state-of-the-art baselines.

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“…Enterprise decision-making units cannot effectively use the existing information, and even make decision-making behavior chaotic. If useful information and knowledge can be mined from a huge database through DL for decision support, enterprises will have a certain degree of competitive advantage [4]. At present, most mainstream financial data analysis systems need to pay for intelligent diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Research on Business Data Analysis and Mining based on Deep Learning

Gao

2023

TCSISR

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At present, the database system can efficiently realize the functions of data entry, query and statistics, but it can't find the rules of relationships in the database, and can't predict the future development trend according to the existing data. Using data mining technology, we can effectively find the regularity behind a large number of data, provide the hidden knowledge and means in the data, and eliminate the phenomenon of data "explosion but lack of knowledge". Based on DL (Deep Learning), this paper studies the analysis and mining of commercial data. In order to better assist investors to evaluate and make decisions on financial data, it puts forward the need to build a reliable and effective financial data forecasting model. In the original data obtained, it is necessary to interpolate by certain methods in data cleaning, and some repeated data will be duplicated. This paper analyzes and makes full use of these massive commercial information through DL, finds out the potential laws of business and market operation, and finds some unknown and hidden useful information from it, which helps enterprises to grasp the development direction of the industry and sales market from a macro perspective and better serve the vast number of consumers.

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Cross-Border E-Commerce Intelligent Information Recommendation System Based on Deep Learning

Cited by 8 publications

References 17 publications

The Development Relationship between Cross-Border e-Commerce and Internet of Things Technology Coupling in Digital Economy Based on Neural Network Model

The Development Relationship between Cross-Border e-Commerce and Internet of Things Technology Coupling in Digital Economy Based on Neural Network Model

Knowledge Graph-aware Deep Interest Extraction Network on Sequential Recommendation

Research on Business Data Analysis and Mining based on Deep Learning

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