Bipartite graph neural network product recommendation model for mobile internet based on user portrait
The product recommendation model can help users quickly locate products of interest from massive in-ternet data.To solve the problem that the current product recommendation model cannot achieve accurate,person-alized and dynamic recommendation at the same time,a bipartite graph neural network product recommendation model for mobile internet based on user portrait is proposed.This model takes the product purchase records of users in a period of time as input,constructs the user and the product in the mobile internet as a bipartite graph,and optimizes the known purchase behaviour of users and the relationship in the purchase behavior among users through graph neural network to obtain network embedding,and then constructs the user portrait to predict the products that users may purchase later.The future flow demand prediction experiment is carried out on the detailed data set about user of China Unicom,and the actual additional flow packet size of users is predicted.Compared with the collaborative filtering method,the excellent performance of the model proposed in this paper is verified.
user portrait,graph neural networkproduct recommendation
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