Poverty/investment slow distribution effect analysis based on Hopfield neural network

With the rapid development of the Internet platform, the Internet economy has become a new situation for evaluating the poverty/investment slow effect. What has brought development to the Internet economy is a variety of e-commerce platforms. However, in the face of huge Internet users and different users' shopping habits, reasonably personalized e-commerce platform recommendations for users can improve the user's website shopping experience. However, due to the huge user data and the diversity of e-commerce platforms, it is a huge challenge to reasonably recommend e-commerce platforms to users. The quality of the e-commerce's personalized recommendations also affects the purchase conversion rate. Clustering algorithm is an algorithm involved in grouping data in machine learning. The same set of data has the same attributes and characteristics, and the attributes or features between different sets of data will be relatively large. In this paper, by using the mean shift clustering algorithm to characterize the behavior data. According to the characteristics of the grouping, we can recommend the e-commerce platform commonly used between the groups. However, using mean shift clustering for personalized recommendation faces the problem of too high user data dimensions. Therefore, we first conduct behavioral analysis of user data by leveraging the well-known Hopfield Neural Network. We define user behavior sequences for user behavior data and classify user behavior. We transform the grouped user behavior into an embedded vector, and linearly transform the embedded vectors of different lengths into the same semantic space. We process the vectors in the semantic space through the self-attention layer and perform mean shift clustering. Experiments show that our method can reduce the complexity of user poverty distribution effect toward complex data and improve the quality of personalized recommendation. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

With the rapid development of the Internet, personalized e-commerce platform recommendations are needed to enhance user shopping experience. Using clustering algorithms for behavioral data representation can effectively recommend commonly used e-commerce platforms.