Knowledge graph recommendation method and system based on improved KGAT model

A knowledge map and recommendation method technology, applied in the field of knowledge map recommendation method and system based on the improved KGAT model, to achieve the effect of improving credibility, excellent performance, and reducing the impact of noise

Inactive Publication Date: 2022-02-15
ZHEJIANG NORMAL UNIVERSITY
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] In view of the above analysis, the present invention aims to disclose a knowledge map recommendation method and system based on the improved KGAT model, which solves the problems of the existing KGAT model and realizes the generation of personalized recommendation reasons

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  • Knowledge graph recommendation method and system based on improved KGAT model
  • Knowledge graph recommendation method and system based on improved KGAT model
  • Knowledge graph recommendation method and system based on improved KGAT model

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Experimental program
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Embodiment 1

[0058] This embodiment discloses a knowledge map recommendation method based on the improved KGAT model, such as figure 1 shown, including:

[0059] Step S1, constructing a domain knowledge graph by constructing a schema layer and a graph database of the knowledge graph;

[0060] Step S2, taking the user as a node of the domain knowledge graph, and adding the historical interaction between the user and the item as the edge of the relationship into the knowledge graph to construct a collaborative knowledge graph;

[0061] Step S3, using the improved KGAT model to learn the collaborative knowledge map to obtain the vectorized representation of users and items and the weight information of adjacent edges of item nodes; the improved KGAT model uses a three-layer MLP network as the attention Assignment function to generate weight information;

[0062] Step S4, sorting all sets of items to be recommended, and selecting the first N items as the final set of items to be recommended ...

Embodiment 2

[0117] This embodiment discloses a knowledge map recommendation system based on the improved KGAT model, such as Figure 5 shown, including:

[0118] The domain knowledge map building module is used to construct and store the schema layer of the knowledge map; import the acquired data layer data into the graph database after knowledge fusion;

[0119] A collaborative knowledge graph construction module, used to use the user as a node of the knowledge graph, and add the historical interaction between the user and the item as an edge representing the relationship to the knowledge graph to construct a collaborative knowledge graph;

[0120] Graph embedding and recommendation model training module, used to learn the collaborative knowledge graph using the KGAT model to obtain a set of all items to be recommended;

[0121] A sorting module, configured to sort all sets of items to be recommended, and select the first N items as the final set of items to be recommended for users;

...

Embodiment 3

[0125] This embodiment verifies the effectiveness of the improved knowledge map proposed by the present invention by comparing with existing methods on multiple data sets.

[0126] 1. Dataset

[0127] This experiment selects three data sets: Amazon-book, LastFM and Yelp2018. The relevant data sets are described in Table 1:

[0128] Table 1

[0129]

[0130]

[0131] 2. Evaluation indicators

[0132] In this embodiment, three model evaluation indexes of accuracy, precision and recall are selected. In the test data set, combined with the prediction results, the recommended ones that are also of interest to users are defined as TP, the ones that are recommended but not of interest to users are defined as FP, and the ones that are not recommended but that are of interest to users are defined as FP. is defined as FN, and those that are not recommended and not of interest to users are defined as TN. As shown in Table 2 below, the above concepts can be described as a confus...

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Abstract

The invention relates to a knowledge graph recommendation method and system based on an improved KGAT model. The method comprises the following steps: constructing a domain knowledge graph; taking the user as a node of a domain knowledge graph, taking historical interaction between the user and an article as an edge of a relationship, adding the historical interaction into the knowledge graph, and constructing a collaborative knowledge graph; using an improved KGAT model to learn the collaborative knowledge graph to obtain vectorized representations of users and articles and weight information of adjacent edges of article nodes; sorting all the to-be-recommended article sets, and selecting the first N articles as a article set to be recommended for the user; generating a recommendation reason based on the sorting result and the weight information of the adjacent edges of the article nodes corresponding to the sorting result in the collaborative knowledge graph; and when the user requests recommendation, generating a recommendation list from the recommendation reasons and the articles, and returning the recommendation list to the user. According to the method and the system, the personalized recommendation list is generated for the user, and meanwhile, the personalized recommendation reason can be generated, so that the credibility of the recommendation result is improved.

Description

technical field [0001] The invention belongs to the technical field of machine learning, and in particular relates to a knowledge graph recommendation method and system based on an improved KGAT model. Background technique [0002] The recommendation system is one of the effective tools to solve the problem of information overload. The system generally includes user portrait modeling, item portrait modeling and recommendation algorithm modules, of which the recommendation algorithm is the core module. Recommendation algorithms are generally divided into demographic-based recommendation, content-based recommendation, and collaborative filtering algorithms. The algorithm based on collaborative filtering is the most widely used and successful algorithm, because it does not depend on the feature data of users or items, and only recommends based on the historical interaction data between users and items, but it still has data sparseness and cold start. question. [0003] The kn...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/36G06K9/62G06N3/04
CPCG06F16/367G06N3/048G06F18/2415
Inventor 朱信忠徐慧英靳林通
Owner ZHEJIANG NORMAL UNIVERSITY
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