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Recommendation algorithm based on knowledge graph

A knowledge graph and recommendation algorithm technology, applied in computing, neural learning methods, instruments, etc., can solve the problems of sparse interactive data and inaccurate recommendation results, and achieve the effect of enhancing recommendation performance and improving recommendation effect.

Active Publication Date: 2022-05-13
浙江辰时科技集团有限公司
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Problems solved by technology

[0005] The present invention provides a recommendation algorithm based on knowledge graphs, which solves the problem of inaccurate recommendation results caused by traditional recommendation algorithms due to sparse interaction data and cold start

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  • Recommendation algorithm based on knowledge graph
  • Recommendation algorithm based on knowledge graph
  • Recommendation algorithm based on knowledge graph

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Embodiment Construction

[0054] The present invention will be specifically introduced below in conjunction with the accompanying drawings and specific embodiments.

[0055] This application discloses a recommendation algorithm based on knowledge graphs, which includes the following steps: constructing knowledge graphs based on application scenarios; constructing KGRN models based on GNN; inputting knowledge graphs into KGRN models to obtain embedding vectors and expressing users' preferences for items according to the embedding vector output The degree of recommendation indicators; according to the recommendation indicators to recommend operations to users. The recommendation algorithm based on the knowledge map of this application constructs a high-quality knowledge map according to the specific scene recommended. After obtaining the historical data of user-item interaction, user-related information, and item-related attributes in the recommendation scenario, we perform knowledge extraction to obtain...

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Abstract

The invention discloses a recommendation algorithm based on a knowledge graph. The recommendation algorithm comprises the following steps: constructing the knowledge graph based on an application scene; constructing a KGRN model based on the GNN; inputting the knowledge graph into a KGRN model to obtain an embedded vector, and outputting a recommendation index which expresses the preference degree of the user to the article according to the embedded vector; and performing recommendation operation to the user according to the recommendation index. According to the recommendation algorithm based on the knowledge graph, the recommendation performance is enhanced in a mode of fusing the knowledge graph and the recommendation system. And the embedding vectors of the nodes are introduced into the click rate prediction model, so that the recommendation effect is further improved.

Description

technical field [0001] The invention belongs to the technical field of computers, and in particular relates to a recommendation algorithm based on a knowledge map. Background technique [0002] Traditional recommendation algorithms are mainly divided into three categories, including content-based recommendation algorithms, collaborative filtering-based recommendation algorithms, and hybrid recommendation algorithms. The recommendation algorithm based on collaborative filtering uses the feedback data of users and item history to mine the correlation between users and items themselves, and make recommendations based on this. This type of algorithm has domain-independent characteristics, so it is widely used. However, collaborative filtering algorithms have data sparsity and cold start problems. The content-based recommendation algorithm can effectively alleviate the cold start problem, and solve the cold start problem of new items by constructing portraits for the content of...

Claims

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Application Information

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IPC IPC(8): G06F16/36G06F16/9535G06N3/04G06N3/08
CPCG06F16/367G06F16/9535G06N3/04G06N3/08
Inventor 袁晓军贾帅琪黄浩秦浪
Owner 浙江辰时科技集团有限公司
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