Commodity information recommendation method and device and storage medium

Abstract

The invention provides a commodity information recommendation method and device and a storage medium, and the method comprises the steps: importing a commodity information data set, and dividing the data set of the commodity information data set to obtain a commodity information training set and a commodity information test set; performing vectorization analysis on the commodity information training set to obtain a knowledge graph matrix and a training set matrix. According to the method, implicit characteristics among data can be effectively mined, accurate recommendation is realized, better differentiation can be realized in mathematics, more effective information in a graph can be mined, rapid recommendation can be realized, accurate recommendation can be realized in a specific scene, certain generalization is realized, the method has a certain effect on different types of data, can effectively improve the recommendation accuracy, has good robustness, can reasonably and accurately recommend commodities to the user, realizes cold start commodity recommendation to the user, and improves the recommendation reliability and accuracy.

Description

technical field [0001] The present invention mainly relates to the technical field of data mining, and in particular to a commodity information recommendation method, device and storage medium. Background technique [0002] In the recommendation system based on knowledge graph, many researchers ignore the adequacy of the relationship between nodes in the graph network and the implicit association information between product nodes, resulting in the effective information in the knowledge graph cannot be fully mined, so it is difficult to improve Recommended accuracy. The recommendation system based on the knowledge graph has a good effect in solving the cold start problem, but the researchers of the recommendation system often ignore whether the relationship vector in the knowledge graph can accurately represent the relationship information between nodes in the knowledge graph, and also It is difficult to deeply dig out the structural information between nodes. Contents of ...

AI Technical Summary

Problems solved by technology

[0002] In the recommendation system based on knowledge graph, many researchers ignore the adequacy of the relationship between nodes in the graph network and the implicit association information between product nodes, resulting in the effective information in the knowledge graph cannot be fully mined, so it is difficult to improve Recommended accuracy

The recommendation system based on the knowledge graph has a good effect in solving the cold start problem, but the researchers of the recommendation system often ignore whether the relationship vector in the knowledge graph can accurately represent the relationship information between nodes in the knowledge graph, and also It is difficult to deeply dig out the structural information between nodes

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