Sequence recommendation method for mining long and short term interests of user based on graph neural network

Abstract

The invention provides a sequence recommendation method for mining long and short term interests of a user based on a graph neural network, which comprises the following steps of: obtaining personal information of the user and a user interaction sequence data set, preprocessing the data set and dividing the data set into a training set and a test set; constructing a sequence recommendation model for mining long and short term interests of the user based on a graph neural network; training the sequence recommendation model for mining the long and short term interests of the user based on the graph neural network; inputting the personal information and the interaction sequence of the to-be-recommended user into the trained sequence recommendation model for mining the long and short term interests of the user based on the graph neural network, calculating the recommendation score of the to-be-recommended item relative to the user, and recommending the item to the user according to the recommendation score; the method solves the problems that long-term and short-term interests of a user cannot be effectively captured in a sequence recommendation scene, and noise is difficult to distinguish.

Description

technical field [0001] The invention belongs to the technical field of sequence recommendation, and in particular relates to a sequence recommendation method for mining users' long-term and short-term interests based on a graph neural network. Background technique [0002] With the growing use of Internet services and mobile devices, Internet users can easily access a large number of online products and services. Although this growth provides users with more available choices, it also makes it difficult for users to pick a favorite item from a large number of candidate items. In order to reduce information overload and meet the diverse needs of users, personalized recommendation systems emerged as the times require, and are playing an increasingly important role in modern society. These systems can provide a personalized experience and serve the individual needs of users. The specific benefits are: (1) helping users easily discover the products they are interested in; (2) c...

AI Technical Summary

Problems solved by technology

[0005] Problems in current deep learning-based sequential recommendation methods: (1) user behaviors in their rich historical sequences are often implicit and noisy preference signals, which cannot fully reflect users’ actual preferences; (2) There are still deficiencies in modeling users' long-term interests and short-term preferences

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