Research of the Context Recommendation Algorithm Based on the Tripartite Graph Model in Complex Systems

Author

Long, Fei

Source

Complexity

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-10-05

Country of Publication

Egypt

No. of Pages

11

Main Subjects

Philosophy

Abstract EN

With the rapid development of information technology, the information overload has become a very serious problem in web information environment.

The personalized recommendation came into being.

Current recommending algorithms, however, are facing a series of challenges.

To solve the problem of the complex context, a new context recommendation algorithm based on the tripartite graph model is proposed for the three-dimensional model in complex systems.

Improving the accuracy of the recommendation by the material diffusion, through the heat conduction to improve the diversity of the recommended objects, and balancing the accuracy and diversity through the integration of resources thus realize the personalized recommendation.

The experimental results show that the proposed context recommendation algorithm based on the tripartite graph model is superior to other traditional recommendation algorithms in recommendation performance.

American Psychological Association (APA)

Long, Fei. 2020. Research of the Context Recommendation Algorithm Based on the Tripartite Graph Model in Complex Systems. Complexity،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1143987

Modern Language Association (MLA)

Long, Fei. Research of the Context Recommendation Algorithm Based on the Tripartite Graph Model in Complex Systems. Complexity No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1143987

American Medical Association (AMA)

Long, Fei. Research of the Context Recommendation Algorithm Based on the Tripartite Graph Model in Complex Systems. Complexity. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1143987

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1143987