<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>To address the issue of cold start and data sparsity in recommendation algorithms, this paper introduces a knowledge graph to assist intelligent recommendation algorithms in exploring. Firstly, it is introduced that the introduction of knowledge graphs into recommendation algorithms can introduce more semantic relationships as well as mining higher-order connection relationships, thus improving the purpose and user satisfaction. Second, the three categories of extant recommendation techniques—embedding-based, path-based, and propagation-based—are compiled. We examine how the current methods extract data from entities and connections in the knowledge graph and contrast the benefits and drawbacks of the three different types of approaches. Furthermore, the AUC metrics on the three datasets improve by 2.9%, 1.6%, and 1.2% over the state-of-the-art baseline, respectively, showing the effectiveness of the KGAT-CI model in using collaborative and knowledge-aware information, according to experiments on intelligent algorithms in the literature. It may be determined that the addition of knowledge graphs can improve the present recommendation algorithms’ accuracy, variety, and interpretability.</p>
</abstract>

To address the issue of cold start and data sparsity in recommendation algorithms, this paper introduces a knowledge graph to assist intelligent recommendation algorithms in exploring. Firstly, it is introduced that the introduction of knowledge graphs into recommendation algorithms can introduce more semantic relationships as well as mining higher-order connection relationships, thus improving the purpose and user satisfaction. Second, the three categories of extant recommendation techniques—embedding-based, path-based, and propagation-based—are compiled. We examine how the current methods extract data from entities and connections in the knowledge graph and contrast the benefits and drawbacks of the three different types of approaches. Furthermore, the AUC metrics on the three datasets improve by 2.9%, 1.6%, and 1.2% over the state-of-the-art baseline, respectively, showing the effectiveness of the KGAT-CI model in using collaborative and knowledge-aware information, according to experiments on intelligent algorithms in the literature. It may be determined that the addition of knowledge graphs can improve the present recommendation algorithms’ accuracy, variety, and interpretability.

Research on intelligent recommendation algorithm of literature based on knowledge graph technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

To address the issue of cold start and data sparsity in recommendation algorithms, this paper introduces a knowledge graph to assist intelligent recommendation...

Research on intelligent recommendation algorithm of literature based on knowledge graph technology

Published Online: Oct 02, 2023

Page range: -

Received: Jan 03, 2023

Accepted: Apr 12, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00457

Keywords
Recommendation algorithm, Knowledge graph, Intelligent recommendation of literature, KGAT-CI model, AUC metric

© 2023 Zhiqiang Yin, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on intelligent recommendation algorithm of literature based on knowledge graph technology

Published Online: Oct 02, 2023

Page range: -

Received: Jan 03, 2023

Accepted: Apr 12, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00457

KeywordsRecommendation algorithm, Knowledge graph, Intelligent recommendation of literature, KGAT-CI model, AUC metric

© 2023 Zhiqiang Yin, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Recommendation algorithm, Knowledge graph, Intelligent recommendation of literature, KGAT-CI model, AUC metric