Personalized recommendation method based on knowledge graph

A technology of knowledge graph and recommendation method, applied in the field of personalized recommendation based on knowledge graph, can solve problems such as poor reliability, poor performance, and poor practicability, and achieve high reliability, high accuracy, and good practicability. Effect

Pending Publication Date: 2022-05-10
CENT SOUTH UNIV
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The collaborative filtering method has good effectiveness and generality, but it cannot model auxiliary information, such as item attributes, user preferences, etc.
thus perform poorly in sparse cases where the number of user and item interactions is low
This makes the existing personalized recommendation methods less reliable and less practical

Method used

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  • Personalized recommendation method based on knowledge graph
  • Personalized recommendation method based on knowledge graph
  • Personalized recommendation method based on knowledge graph

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Embodiment Construction

[0042] as Figure 1 The method flow diagram of the method of the invention is shown: the personalized recommendation method based on knowledge map provided by the invention comprises the following steps:

[0043] S1. Obtain the user's data information;

[0044] S2. Constructing a knowledge map according to the user data information obtained in step S1; Specifically, the user item interaction map and the item knowledge map are integrated into a collaborative knowledge map; It includes the following steps:

[0045] In addition to extracting the interaction between articles, the method of the invention also extracts article knowledge (such as theme, director and starring in the film data set) as knowledge map data; Then, filter the entities whose frequency is lower than the set value and retain the relationship that appears in at least n triples; Use 10 core, 20 core and 30 core settings at the same time, so as to ensure that each item has at least 10 interaction nodes, 20 interaction...

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Abstract

The invention discloses a personalized recommendation method based on a knowledge graph. The method comprises the following steps: acquiring data information of a user and constructing the knowledge graph; encoding entity nodes of the mapping knowledge domain and generating embedded vector representation for each entity and relationship in the mapping knowledge domain; performing message aggregation to obtain the feature representation of the user and the feature representation of the article; and calculating the probability that each article is recommended to the user and completing personalized recommendation based on the knowledge graph. According to the method, the defect that user-project interaction records are mutually independent is overcome, and project attribute-based collaborative information of users can be obtained; the method enables the model to capture rich semantics and high-order connectivity based on inter-node feature interaction in the collaborative knowledge graph; therefore, the method is high in reliability, good in practicability and high in accuracy.

Description

technical field [0001] The invention belongs to the computer field, in particular to a personalized recommendation method based on knowledge map. Background technology [0002] With the development of economy and technology and the advent of the information age, personalized recommendation technology has been widely used in people's production and life, which has brought endless convenience to people's production and life. With the development of information technology and Internet, people have gradually entered the era of information overload from the era of information scarcity. It is difficult for consumers to find the information they are interested in from a large amount of information (goods), and information producers to make the information they produce stand out and get attention. The task of recommendation method is to connect users and information (items). At the same time, another problem to be solved by the recommendation method is to explore the user's behavior and ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9536G06F16/36G06F40/30G06Q30/06
CPCG06F16/9536G06F16/367G06F40/30G06Q30/0631
Inventor 张师超左铠宁章成源
Owner CENT SOUTH UNIV
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