Binding recommendation method and system based on graph convolutional neural network

Abstract

The embodiment of the invention provides a binding recommendation method and system based on a graph convolutional neural network. The method comprises the steps: acquiring historical interaction dataof a user and binding, historical interaction data of the user and an article and affiliation data of the binding and the article; inputting the data into the binding recommendation model to obtain auser and binding interaction possibility recommendation result output by the binding recommendation model, wherein the bundled recommendation model is obtained by constructing a unified heterogeneousgraph based on a user data set, a bundled data set and an article data set, extracting article hierarchical graph convolution propagation features and bundled hierarchical graph convolution propagation features, then performing joint prediction and feature connection, and performing training based on a difficult negative sample learning strategy. According to the embodiment of the invention, theinteraction relationship and subordination relationship among the user, the bundle and the article are reconstructed into the graph, and three associated entity representations are learned from a complex topological structure and high-order connectivity by utilizing the powerful capability of the graph neural network, so that better recommendation performance can be achieved.

Description

technical field [0001] The present invention relates to the technical field of bundling recommendation, in particular to a bundling recommendation method and system based on a graph convolutional neural network. Background technique [0002] Bundled recommendation is defined as a set of bundled items that are intended to be recommended for overall consumption by users. The prevalence of bundled items on e-commerce and content platforms makes bundled recommendation an important task, which can not only avoid users’ monotonous choices but also improve user experience. experience, but also increase business sales by scaling order sizes. Since a bundle consists of multiple items, the attractiveness of the bundle depends on the items within the bundle, with the attractiveness of each item within the bundle being affected by the other items displayed together in the bundle. In addition, users need to be satisfied with most items in the bundle, which means that the interaction bet...

AI Technical Summary

Problems solved by technology

[0008] The embodiment of the present invention provides a method and system for bundling recommendation based on graph convolutional neural network, which is used to solve the problem that the weight relationship among users, bundles and items cannot be balanced when bundling recommendation in the prior art, and the corresponding decision-making reference relationship is not enough Comprehensive, leading to inaccurate and objective recommendation results

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