Information recommendation method and device based on knowledge graph, equipment and storage medium

Abstract

The invention relates to the technical field of artificial intelligence, and discloses an information recommendation method and device based on a knowledge graph, equipment and a storage medium, and the method comprises the steps: obtaining initial data, recognizing the relationship between entities in the initial data, and constructing the knowledge graph; when a target customer is determined, extracting initial data of the target customer from the knowledge graph, constructing a sub-graph, then training a graph convolutional neural network GCN by adopting the sub-graph and a pre-constructedproduct feature vector, inputting product information data in basic data into the trained graph convolutional neural network GCN, and carrying out binary classification processing; and obtaining a selection probability of each product, selecting the corresponding product information data as to-be-recommended information according to the selection probability, and pushing the to-be-recommended information to the target client. The invention also relates to blockchain technology. Initial data is stored in the blockchain. According to the invention, the knowledge graph is constructed to improve the efficiency of information recommendation.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, and in particular to an information recommendation method, device, device, and storage medium based on knowledge graphs. Background technique [0002] Information recommendation is a way to push data information that users care about to users according to user preferences. Accurate information recommendation, on the one hand, is beneficial for data providers to push their own data information to users; on the other hand, it is also conducive to users get the information they want. Recommender systems have grown in popularity in recent years and are used in a variety of different fields, including movies, music, news, books, and more. For example, e-commerce platforms also have their own special recommendation systems to provide customers with products they may like. With reasonable settings, it can effectively increase profits, click-through rates, conversion rates, et...

AI Technical Summary

Problems solved by technology

However, its method of analyzing user preference information is defined only according to the user’s selection of a certain product, without fully considering the relationship between product attributes and the relationship between customers and products, resulting in deviations in the obtained user preference information , so that the conversion rate of recommended information is not high, resulting in low information recommendation efficiency

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