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Method for carrying out personalized recommendation on commodities by fusing knowledge graph

A technology of knowledge graph and knowledge graph, which is applied in the field of personalized product recommendation by integrating knowledge graph, which can solve problems such as single recommendation results and insufficient mining of user product features, and achieve the effect of accurate recommendation results

Pending Publication Date: 2021-01-15
HARBIN ENG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The present invention aims at the problems of single recommendation results and insufficient mining of user product features in the previous methods. The present invention makes full use of the knowledge information of the knowledge map, and the present invention provides the following technical solutions:

Method used

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  • Method for carrying out personalized recommendation on commodities by fusing knowledge graph
  • Method for carrying out personalized recommendation on commodities by fusing knowledge graph
  • Method for carrying out personalized recommendation on commodities by fusing knowledge graph

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specific Embodiment 1

[0054] according to Figure 1 to Figure 2 As shown, the present invention provides a method of merging knowledge graphs for personalized recommendation of commodities, including the following steps:

[0055] Step 1: Obtain the user's historical behavior data, obtain the product entity knowledge according to the product name in the user's historical behavior data, and generate a product knowledge map;

[0056] The specific step 1 is:

[0057] Step 1.1: Obtain user historical behavior data, analyze user historical interaction data, extract product list information that interacts with users, perform entity matching through knowledge graphs, query and download product knowledge information in the product list;

[0058] Step 1.2: Extract keywords from the product knowledge information in the knowledge map to obtain keywords containing product entities and relationships;

[0059] Step 1.3: According to the keywords of the obtained product entity and relationship, create a product ...

specific Embodiment 2

[0096] according to figure 1 As shown, the present invention provides a personalized product recommendation method that integrates knowledge graphs, including the following steps:

[0097] Step 1: Obtain user historical behavior data, obtain relevant product entity knowledge according to the product name in user historical behavior data, and generate a product knowledge graph;

[0098] Step 1.1: Obtain user historical behavior data, analyze user historical interaction data, extract product list information that interacts with users, perform entity matching through knowledge graphs, query and download relevant product knowledge information in the product list. For example, obtain the list information of movie products purchased by the user: . Analyze the user's historical interaction data, and match the product entity through the DBpedia online knowledge map according to the product list information purchased by the user, and query and download the relevant product knowledge e...

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Abstract

The invention relates to a method for carrying out personalized recommendation on commodities by fusing a knowledge graph. The invention relates to the technical field of machine learning for personalized recommendation of commodities, and the method comprises the steps: obtaining the historical behavior data of a user, and generating a commodity knowledge graph; fusing the historical behavior data of the user with the commodity knowledge graph to construct a collaborative knowledge graph; adopting a representation learning method in machine learning to obtain vector embedding representation of nodes and relationships in the collaborative knowledge graph; embedding vectors of nodes and relations in the collaborative knowledge graph and inputting historical behavior data of the user into agraph convolutional neural network model based on an attention mechanism to form vector embedding representation of new nodes and relations containing neighborhood information; and defining a score function, calculating the probability that the user likes the commodity through the user and commodity vector embedding representation of the fused neighborhood information output by the graph convolutional neural network model, and performing sorting according to a prediction result to obtain a recommendation list.

Description

technical field [0001] The invention relates to the technical field of machine learning for personalized recommendation of commodities, and relates to a method for personalized recommendation of commodities by integrating knowledge graphs. Background technique [0002] In today's era, with the rapid development of information technology and Internet technology, the amount of network information is showing an exponential increase in rapid growth. Faced with the massive growth of Internet information, it is becoming more and more difficult for people to find the most suitable and truly effective information, and the utilization rate of information has not increased but has decreased. Online platforms for network information services are also facing the same problem. How to recommend content that users are most interested in among a large number of information has become a common challenge for service platforms. In order to solve these problems, many experts and scholars at ho...

Claims

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Application Information

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IPC IPC(8): G06Q30/06G06F16/95G06N3/04
CPCG06Q30/0631G06F16/9535G06N3/045
Inventor 张海涛李一豪宋洪涛韩启龙隋珊珊张慧
Owner HARBIN ENG UNIV
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