Post Profiles Research Based on Electric Power Major

Abstract

In the era of big data, how to effectively use information resources under the condition of information overload has been the focus of academia and industry. As an important data analysis method, user profile technology is widely used in the field of big data, including the field of recommendation system. Based on the background of electric power post training, this paper constructs the post knowledge thesaurus of electric power industry, uses the word segmentation tool-Jieba to segment the job description, and processes the text after the word segmentation combined with Term Frequency–Inverse Document Frequency (TF-IDF) algorithm. Then, the post profiles at all levels are displayed by Wordcloud visualization tool. Finally, the effectiveness of our method is proved by experiments. This work provides a basis for the intelligent recommendation of the best learning materials in various positions and auxiliary online examination technology for employees in the future.