Research and Implementation of Content Recommendation Algorithm Optimization Based on Multi-Source Information Fusion

<p>Linlin He, Yanbin Long</p>

doi:10.25236/AJMS.2024.050204

Academic Journal of Mathematical Sciences, 2024, 5(2); doi: 10.25236/AJMS.2024.050204.

Research and Implementation of Content Recommendation Algorithm Optimization Based on Multi-Source Information Fusion

Author(s)

Linlin He, Yanbin Long

Corresponding Author:

Linlin He

Affiliation(s)

University of Science and Technology Liaoning, Anshan, China

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Abstract

With the rapid development of information technology, how to provide personalized content recommendation for users from massive data has become a research hotspot. This paper proposes an optimization method of content recommendation algorithm based on multi-source information fusion, which improves the accuracy of recommendation and user satisfaction by combining user behavior data, content characteristics and external auxiliary information. This paper first introduces the research background and significance of content recommendation algorithm, then elaborates the design idea and implementation process of the proposed algorithm in detail, and carries out experimental verification through Java programming language.

Keywords

multi-source information fusion; Content recommendation; Algorithm optimization; Java implementation

Cite This Paper

Linlin He, Yanbin Long. Research and Implementation of Content Recommendation Algorithm Optimization Based on Multi-Source Information Fusion. Academic Journal of Mathematical Sciences (2024) Vol. 5, Issue 2: 18-24. https://doi.org/10.25236/AJMS.2024.050204.

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