Abstract
The recommendation system can calculate the similarity between users and items in the system, which is based on different computational methods, analyzing of calculation results, and then calculates the items that may interest the users and recommend those items to users. Collaborative filtering algorithm is a popular algorithm in academia and industry, but it does have certain shortcomings such as cold start and sparse data. In this paper, a hybrid recommendation algorithm is proposed and applied to an employment service system by using job postings from websites obtained by web crawler as the dataset. The experimental result shows that the hybrid recommendation algorithm is able to the accuracy of employment information recommendation to some extent and meet the personalized needs of job-seeking users.
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
References
Anderson, C.: The long tail: why the future of business is selling less of more. J. Prod. Innov. Manag. 24(3), 274–276 (2005)
Alhaj, R., Rokne, J.: Encyclopedia of Social Network Analysis and Mining. Springer, New York (2014)
Linden, G., Smith, B., York, K.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)
Hong, W., Zheng, S., Wang, H., et al.: Dynamic user profile-based job recommender system. In: International Conference on Computer Science and Education, pp. 1499–1503 (2013)
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)
Balabanovic, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40(3), 66–72 (1997)
Bellogin, A., De Vries, A.P.: Understanding similarity metrics in neighbour-based recommender systems. In: International Conference on the Theory of Information Retrieval, pp. 48–54 (2013)
Bradley, K., Rafter, R., Smyth, B., et al.: Case-based user profiling for content personalisation. In: Adaptive Hypermedia and Adaptive Web Based Systems, pp. 62–72 (2000)
Buckley, C., Voorhees, E.M.: Evaluating evaluation measure stability. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, vol. 51, no. 2, pp. 33–40 (2000)
Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Inter. 12(4), 331–370 (2002)
Deshpande, M., Karypis, G.: Item-based top-N recommendation algorithms. ACM Trans. Inf. Syst. 22(1), 143–177 (2004)
Chen, C.: The recommendation system based on two-sides selection between college graduates and employers. Southwest University of Science and Technology, SiChuan Province, pp. 51–59 (2014). (in Chinese)
Zhang, Y.: Graduate employment recommendation system based on collaborative filtering. Nankai University, Tianjin, pp. 1–19 (2014). (in Chinese)
Acknowledgements
This work was supported by WJY2018002. (Online education & teaching research project of ECUST).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Dong, Z., Leng, C., Zheng, H. (2021). Employment Service System Based on Hybrid Recommendation Algorithm. In: Atiquzzaman, M., Yen, N., Xu, Z. (eds) Big Data Analytics for Cyber-Physical System in Smart City. BDCPS 2020. Advances in Intelligent Systems and Computing, vol 1303. Springer, Singapore. https://doi.org/10.1007/978-981-33-4572-0_54
Download citation
DOI: https://doi.org/10.1007/978-981-33-4572-0_54
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-4573-7
Online ISBN: 978-981-33-4572-0
eBook Packages: Computer ScienceComputer Science (R0)