Research On Precision Marketing Of Taobao E-commerce Platform Based On CapsNet

With the continuous development of technology,after solving the "last mile" problem in logistics and achieving large-scale coverage in 5G networks,nowadays,shopping by using e-commerce platforms has become the most important method for Chinese consumers.An increasing number of online consumers have led to the fact that e-commerce industry paid more and more attention to precise marketing.How to accurately predict user preferences,achieve efficient product recommendation in industrial scenarios,and improve the conversion rate of platform recommendations are an essence issues of current major e-commerce platforms.This paper first expounds the research background and research significance of the subject,and proposes the research purpose of this subject according to the current research status.In addition,it also summarizes the current research status at home and abroad,and provides a detailed description of the commonly used recommendation algorithms based on deep learning.Elaborate and outline the relevant influencing factors on user behavior.Secondly,this paper selects the "Taobao User Behavior Dataset" as the research object.This dataset contains users’ implicit feedback behaviors,which can better reflect the influence of users’ browsing,favorites,and add-buying behaviors on their purchasing behaviors.Through data analysis,data cleaning,data visualization,feature engineering,and feature selection on the dataset,this paper extracts data features from four levels,including user level,commodity level,commodity feature and combination feature level.The more important features are screened out from these features to ensure better performance on subsequent models.Finally,the F1-Score indicator is chosen to evaluate the CNN model and the capsule network model respectively.The experimental results show that the model based on the capsule network has a good prediction effect on the data set of this experiment.Calculate the similarity between users,get the TOP-5 similar neighbors of the target user,and then generate the set of commodities that the target user is most likely to buy.This paper takes the user behavior of Taobao e-commerce platform as the research object,builds a prediction model based on capsule network,and realizes product recommendation based on this.Therefore,this paper has certain practical significance,and provides a certain reference value and reference significance for the e-commerce platform to recommend products to users.