Abstract

With the enormous growth of the content and users of social network, user portrait based on social network have been widely used in industrial applications such as artificial intelligence, recommendation systems, etc. The issue has been extensively studied, and most of them have focused on user attribute mining and behavior prediction. However, there is little research on the intrinsic relationship between big data-based interests and skills and occupational adaptability. To clarify this issue, in this article, we first collect a large amount of user data from LinkedIn. Then, we filter high-frequency interests and skills, and explore relevant characteristics of interests and skills through relevant analytical methods, found that there are many association rules. Next, the users are grouped according to the characteristics of occupational adaptability, and the relationship between the association rules of interests and skills and user adaptability is studied. Finally, designed an occupational adaptive classifier based on association rules. This article reveals the connections and dependencies between human interests and professional skills, as well as the impact of these association rules on user career adaptability, also discusses the prospects of these results in industrial applications. We hope that our research results will provide a solid theoretical foundation for other areas of research and industrial applications, such as career adaptability judgment, interest training, career development, career recommendation, etc.

Highlights

  • S OCIAL networks have entered our live, as people connect, express themselves, and share their lives on social media, the big data of social network hides the user’s information, which makes user portraits a hot topic

  • The confidence of association rule “basketball→leadership” is as high as 62.93%, its support is as high as 3.96%, and its lift degree is up to 184.05%, which shows that the human interest “basketball” and the skill “leadership” are highly relevant

  • Through the empirical correlation analysis, we find that there is a great deal of association relationship among human interests and skills, and some association rules have high

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Summary

INTRODUCTION

S OCIAL networks have entered our live, as people connect, express themselves, and share their lives on social media, the big data of social network hides the user’s information, which makes user portraits a hot topic. LinkedIn [6] is a globally popular professional social network with hundreds of millions of registered members These members will provide personal information at the time of registration such as interests and professional skills, so we can study the intrinsic relationship, relevance, and cluster characteristics of hobbies and professional skills, which laid the foundation for our study of occupational adaptability. Two-dimensional association rules between interests and skills are mined to provide data support for researching user occupational adaptability. We give more related work related with user portrait and association analysis, Section VI concludes this article We give more related work related with user portrait and association analysis in Section V, Section VI concludes this article

Data Collection
Preprocess of Data
Correlation Analysis Model
Correlation Analysis
Association Rules of Interests and Skills
Relationship Between the Number of Job-Hopping and Association Rules Matching
Industrial Applications
Findings
CONCLUSION
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