Interest point recommendation method based on graph neural network

Abstract

The invention discloses an interest point recommendation method based on a graph neural network, and the method comprises the steps: constructing a user-interest point interaction graph and a user social graph, enabling the graph neural network to learn graph structure information, and integrating cooperation information and social information in an embedded vector of a user; clustering the interest points according to geographic positions by adopting a k-means algorithm, embedding clustering results into vectors, connecting embedded vectors obtained in the user-interest point interaction graph, and inputting the embedded vectors into a neural network to obtain interest point embedded vectors; and constructing a neural network model, simulating a matrix decomposition method in machine learning, and inputting the embedded vectors of the user and the interest points into the neural network model to perform score prediction according to historical scores of the user. Cooperation information and information in a social network are embedded into vector representation of a user, the cooperation information and position information of interest points are embedded into vector representation of the interest points, and the vector representation of the user and the vector representation of the interest points are input into a neural network for recommendation.

Description

technical field [0001] The invention belongs to the technical field of neural networks and recommendation systems, in particular to a point-of-interest recommendation method based on a graph neural network. Background technique [0002] In recent years, with the rapid development of mobile Internet technology and smart devices, users can easily obtain personal real-time location information. At the same time, location-based social networks (LBSNs) and point-of-interest recommendation technology (POI recommendation) have also received extensive attention. Collaborative filtering is a method widely used in recommendation systems. Its basic idea is to find other users who are similar to a user's preferences by analyzing the user's interest preferences, and then integrate the evaluations of these similar users on the points of interest to predict the user's interest points. degree of preference. Learning vector representations of users and points of interest is a research hotsp...

AI Technical Summary

Problems solved by technology

Many existing models only consider the friends in the user's social network, ignoring that the user's decision-making behavior may be affected by the friends of the user's friends. The information transfer between users is not limited to the user's friends, the information transfer between users There is a phenomenon of information diffusion, and only considering the friends in the user's social network is not enough to capture the impact of the information transmitted in the social network

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