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.
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This work was supported by WJY2018002. (Online education & teaching research project of ECUST).
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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
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DOI: https://doi.org/10.1007/978-981-33-4572-0_54
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