Application of Improved Collaborative Filtering in the Recommendation of E-commerce Commodities

Chang, Dan; Gui, Hao Yu; Fan, Rui; Fan, Ze Zhou; Tian, Ji

doi:10.15837/ijccc.2019.4.3594

Cited by 21 publications

(13 citation statements)

References 26 publications

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“…However, due to the large number of Internet users using social media and the unique shopping experience of social e-commerce, social e-commerce will be the trend of future e-commerce development. With the rise of online celebrity live broadcasts, social e-commerce has become an important type of e-commerce [2].…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…The item-based collaborative filtering method analyzes the similarity relationship between items and then provides users with similar items. The model-based collaborative filtering is based on recommendations which are based on the relationship between existing item features and user features [ 8 ].…”

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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“…This paper mines data with the help of the SVM algorithm [22][23][24][25][26]. First, the abnormal data were cleaned from the education big data.…”

Section: Data Mining Algorithmmentioning

confidence: 99%

A Knowledge Image Construction Method for Effective Information Filtering and Mining From Education Big Data

et al. 2021

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Traditionally, education resources are shared insufficiently, and updated slowly; the education data are not utilized adequately. What is worse, the conventional information filtering method cannot effectively mine desired information, if the big data has a heavy noise. This article presents an information mining method from education big data, on the basis of support vector machine (SVM), and cleans the sampled abnormal data through data integration and conversion. Besides, the authors presented a method that automatically builds education knowledge image. Based on the filtered and mined education data, a neural network was designed to retrieve the themes of classroom knowledge, and the education correlations between these notions were recognized from the evaluation data by possibility correlation rules. The results show our method achieved excellent results on teaching notion retrieval and education correlation recognition.

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Application of Improved Collaborative Filtering in the Recommendation of E-commerce Commodities

Cited by 21 publications

References 26 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

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

A Knowledge Image Construction Method for Effective Information Filtering and Mining From Education Big Data

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