Recommendation method and device based on knowledge graph, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a recommendation method and device based on a knowledge graph, electronic equipment and a storage medium, and the method comprises the steps of obtaining target text information, and searching an entity node matched with the target text information in a pre-constructed knowledge graph; determining a neighbor entity node of the entity node in the knowledge graph, and determining an associated entity node of the neighbor entity node in the knowledge graph; searching a neighbor entity corresponding to the neighbor entity node, and searching an associated entity corresponding to the associated entity node; and recommending the neighbor entity and the associated entity. According to the embodiment of the invention, the knowledge graph is introduced, and recommendation is performed through the knowledge graph, so that a large amount of unknown information can be prevented from being predicted by using less common data among a small amount of users or articles, the possibility of data over-fitting is avoided, and the recommendation accuracy is improved.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of recommendation, and in particular, to a recommendation method, device, electronic device, and storage medium based on knowledge graphs. Background technique [0002] In the Internet age, the Internet has become an indispensable part of people's lives and an important way for people to obtain information resources. With the development of the Internet, the resources on the network have also exploded and exponentially increased, leading to the problem of information overload. How to present the information that users are interested in has become a hot and difficult research point. The personalized recommendation system is user-oriented, realizes information filtering, solves the problem of information overload, and accurately locates user preference information. [0003] In related technologies, traditional personalized recommendation systems are based on collaborative filtering of co...

AI Technical Summary

Problems solved by technology

[0004] However, in practical application scenarios, there are few common data between a small number of users or items. For example, there are only 10 users in the database, and there is only one common data between user N and user M: age, but the actual number of movies is 10,000 One, it is easy to use a small number of common data between users or items to predict a large amount of unknown information, increase the possibility of data overfitting, and reduce the accuracy of recommendation

Images