DLGNN: A Double-layer Graph Neural Network Model Incorporating Shopping Sequence Information for Commodity Recommendation

Wei, Chuyuan; Niu, Jianwei; Guo, Yanqin

doi:10.18494/sam.2020.3056

Cited by 9 publications

(9 citation statements)

References 24 publications

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“…Literature [16] found that customer fit in live broadcast can promote customers' consumption of virtual gifts. rough literature reading, we have a deep understanding of the concept of customer fit.…”

Section: Related Workmentioning

confidence: 99%

Model Construction of Hierarchical Polarization Characteristics Combined with Social E-Commerce Consumer Behavior

Zhang

Yang

2022

Security and Communication Networks

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In order to analyze the consumer behavior of social e-commerce, this article attempts to explore the irrational consumer behavior factors that affect the audience in the live broadcast of e-commerce Internet celebrities. This paper predicts and analyzes the consumption behavior of social e-commerce and combines the characteristics of hierarchical planning to intelligently process the system to improve the effectiveness of the system. Moreover, this paper constructs a social e-commerce consumer behavior prediction model based on hierarchical polarization characteristics, researches and analyzes multiple factors and performance, and builds an intelligent simulation model. The analysis of the test results shows that the social e-commerce consumer behavior prediction model based on the hierarchical polarization characteristics proposed in this article is effective and can play an important role in the prediction of social consumer behavior.

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Section: Related Workmentioning

confidence: 99%

Model Construction of Hierarchical Polarization Characteristics Combined with Social E-Commerce Consumer Behavior

Zhang

Yang

2022

Security and Communication Networks

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“…At this stage, the manufacturer can adjust the product through the consumer's feedback information. e Howard-Sheth model defines customer satisfaction as a kind of cognition of consumers and cognition of whether their pay and return are appropriate, if appropriate, the degree of satisfaction will be higher [14]. e shopping decision-making process of consumers is the process of consumers' cognition of the purchased products.…”

Section: Related Workmentioning

confidence: 99%

Personalized Recommendation of Online Shopping Products Based on Online Fast Learning through Latent Factor Model

Shi¹

2022

Advances in Multimedia

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In order to improve the personalized recommendation effect of online shopping products, this article combines online fast learning through latent factor model to construct a personalized virtual planning recommendation system for online shopping products. Moreover, this article improves on the ONMTF model. In the problem of cross-domain recommendation, this article clusters users and items in each data domain with hidden scoring patterns and learns common scoring patterns that can be shared between different data domains to deal with the data sparse problem that often occurs in recommender systems. The experimental research results show that cross-domain recommendation can indeed use the implicit semantics or topics between domains to share information and knowledge, thereby improving the accuracy of recommendation.

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“…It can not only predict the target user's preferences, for items can also provide explanations about the characteristics of specific items. Wei et al [ 16 ] propose a recommendation method that uses tags as features and explain to the user why the recommended movie is related to him based on the features. At the same time, user research experiments have been conducted, and the results show that providing feature-based explanations for the recommendation results can help improve the effectiveness of the recommendation results.…”

Section: Related Workmentioning

confidence: 99%

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

2022

Computational Intelligence and Neuroscience

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In order to improve the effect of cross-border e-commerce intelligent information recommendation, this paper applies deep learning to the intelligent information processing and intelligent recommendation of e-commerce and proposes an improved version of the topic model to solve the problem of feature extraction of the text of the recommendation system. In order to deal with translation problems, this paper proposes an end-to-end sequence-to-sequence learning method. In addition, this study uses the long tail theory to excavate the mass commodities in the niche and recommends these products to users as suggestions. Finally, this paper proposes a niche product recommendation algorithm based on the graph search strategy based on the graph model. The experiment shows that the cross-border e-commerce intelligent information recommendation system based on deep learning proposed in this paper has a good recommendation effect and meets the recommendation needs of cross-border e-commerce.

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DLGNN: A Double-layer Graph Neural Network Model Incorporating Shopping Sequence Information for Commodity Recommendation

Cited by 9 publications

References 24 publications

Model Construction of Hierarchical Polarization Characteristics Combined with Social E-Commerce Consumer Behavior

Model Construction of Hierarchical Polarization Characteristics Combined with Social E-Commerce Consumer Behavior

Personalized Recommendation of Online Shopping Products Based on Online Fast Learning through Latent Factor Model

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

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