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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Liang, R., Meng, X., Zhou, L., Peng, N.: Status quo and prospect of distribution network fault location. Electric Power Eng. Technol. 37(6), 20–27 (2018). (in Chinese)
Park, W., Kim, W., Kang, S., Lee, H., Kim, Y.-K.: Personalized digital e-library service using users’ profile information. In: Gonzalo, J., Thanos, C., Verdejo, M.F., Carrasco, R.C. (eds.) ECDL 2006. LNCS, vol. 4172, pp. 528–531. Springer, Heidelberg (2006). https://doi.org/10.1007/11863878_60
Rimitha, S.R., Abburu, V., Kiranmai, A., Chandrasekaran, K.: Ontologies to model user profiles in personalized job recommendation. In: 2018 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), Mangalore (Mangaluru), India, pp. 98–103 (2018)
Dayane, C.M.F.C., Ronaldo, C.M.C., et al: Data mining on LinkedIn data to define professional profile via MineraSkill methodology. In: 2017 12th Iberian Conference on Information Systems and Technologies (CISTI), Lisbon, pp. 1–6 (2017)
Ibrahim, M.E., Yang, Y., Ndzi, D., et al.: Ontology-based personalized course recommendation framework. IEEE Access 7, 5180–5199 (2018)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)
Yang, W., Wang, Z., You, M.: An improved collaborative filtering method for recommendations’ generation (2004)
Sarwar, B., Karypis, G., Konstan, J., et al.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295 (2001)
Lee, Y.: Recommendation system using collaborative filtering. M.S. thesis, Dept. Comput. Sci., San Jose State Univ., San Jose, CA, USA, vol. 49 (2015)
**, X., Mobasher, B.: Using semantic similarity to enhance item-based collaborative filtering. In: Proceedings of the 2nd IASTED International Conference on Information and Knowledge Sharing, pp. 1–6 (2003)
Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_10
Tarus, J.K., Niu, Z., Mustafa, G.: Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning. Artif. Intell. Rev. 50(1), 21–48 (2017)
Chang, P.C., Lin, C.H., Chen, M.H.: A hybrid course recommendation system by integrating collaborative filtering and artificial immune systems. Algorithms 9(3), 47 (2016)
Zhang, H., Yang, H., Huang, T., et al.: DBNCF: personalized courses recommendation system based on DBN in MOOC environment. In: 2017 International Symposium on Educational Technology (ISET). IEEE (2017)
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl. Syst. 46, 109–132 (2013)
Amit, S., Sherwein, V., Vijaya, S.D.: Analytical recommendation model using directed graphs for employee and organization. In: 2018 8th International Conference on Intelligent Systems, Modelling and Simulation (ISMS), Kuala Lumpur, Malaysia, pp. 84–89 (2018)
Akshay, G., Vinay, K.R.K., Karthikeyan, P.: Generating unified candidate skill graph for career path recommendation. In: 2018 IEEE International Conference on Data Mining Workshops (ICDMW), Singapore, Singapore, pp. 328–333 (2018)
Song, S., Qiao, X., Bu, Q., Song, L., Gao, L.: Research on the technical scheme of outdoor-layout relay protection in smart substation. Electric Power Eng. Technol. 37(02), 83–88 (2018). (in Chinese)
Hou, X., Wang, B., Liu, W., Zhou, J.: Approach of relay protection setting remote operation based on operating files. Electric Power Eng. Technol. 37(01), 147–152 (2018). (in Chinese)
Zou, D., Chen, G., Xu, X., Zhao, Y.: Architecture design of automatic voltage control system for active distribution network. Electric Power Eng. Technol. 38(04), 42–47 (2019). (in Chinese)
Thomas, R., Jun, W.: TF-IDF uncovered: a study of theories and probabilities. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2008), pp. 435–442. ACM, New York (2008)
Acknowledgment
The authors would like to acknowledge the support provided by the Jiangsu Electric Power Company Technology Project (NO. J2019023).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Dai, W. et al. (2019). Post Profiles Research Based on Electric Power Major. In: Ning, H. (eds) Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health. CyberDI CyberLife 2019 2019. Communications in Computer and Information Science, vol 1137. Springer, Singapore. https://doi.org/10.1007/978-981-15-1922-2_30
Download citation
DOI: https://doi.org/10.1007/978-981-15-1922-2_30
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1921-5
Online ISBN: 978-981-15-1922-2
eBook Packages: Computer ScienceComputer Science (R0)