HYBRID JOB RECOMMENDATION SYSTEM PADA PERGURUAN TINGGI
Abstract
ABSTRACT
Job vacancies are information that is needed by all job seekers, especially students and alumni of a college, many universities already have a career center system, but most of the systems are just information without any processing in it. In this paper, we will discuss how job recommendations are made and presented back to job seekers by combining the Cosine Similarity and Alternating Least Squares algorithms. Based on the experiments conducted, the average Precision value is 0.75.
Keywords: Vacancies, Jobs, Hybrid Recommendation, Alternating Least Squares
ABSTRAK
Lowongan pekerjaan merupakan suatu informasi yang sangat diperlukan oleh semua para pencari kerja terlebih lagi para mahasiswa maupun alumni suatu perguruan tinggi, banyak perguruan tinggi yang sudah mempunyai suatu sistem career center namun kebanyakan dari sistem hanya sebagai informasi saja tanpa ada suatu pemrosesan didalamnya. Dalam paper ini akan membahas tentang bagaimana rekomendasi pekerjaan dibuat dan ditampilkan kembali kepada para pencari kerja dengan menggabungkan algoritma Cosine Similarity dan Alternating Least Squares. Berdasarkan percobaan yang dilakukan dihasilkan rata – rata nilai Precesion sebesar 0.75
Kata Kunci: Lowongan, Pekerjaan, Hybrid Recommendation, Alternating Least Squares
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References
X. Guo, H. Jerbi, and M. P. O. Mahony, “An Analysis Framework for Content-based Job Recommendation,” 22nd International Conference on Case-Based Reasoning, p. 4, 2013.
J. E. Manurung and E. T. Putri, “Penentuan Minat Bakat Menggunakan Metode Bayes Berbasiss Web,” KONVERGENSI, vol. 16, no. 2, pp. 80–89, 2020.
A. Zaroor, M. Maree, and M. Sabha, “A hybrid approach to conceptual classification and ranking of resumes and their corresponding job posts,” Smart Innovation, Systems and Technologies, vol. 72, pp. 107–119, 2018.
N. D. Almalis, G. A. Tsihrintzis, and N. Karagiannis, “A content based approach for recommending personnel for job positions,” IISA 2014 - 5th International Conference on Information, Intelligence, Systems and Applications, pp. 45–49, 2014.
E. Ronando, M. Yasa, and E. Indasyah, “Sistem Prediksi Kepribadian Manusia Berdasarkan Status Media Sosial Menggunakan Support Vector Machine,” KONVERGENSI, vol. 17, no. 1, pp. 13–22, 2021.
W. Shalaby et al., “Help me find a job: A graph-based approach for job recommendation at scale,” Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017, vol. 2018-January, pp. 1544–1553, 2017.
R. Liu, Y. Ouyang, W. Rong, X. Song, C. Tang, and Z. Xiong, “Rating prediction based job recommendation service for college students,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9790, pp. 453–467, 2016.
R. Liu, W. Rong, Y. Ouyang, and Z. Xiong, “A hierarchical similarity based job recommendation service framework for university students,” Frontiers of Computer Science, vol. 11, no. 5, pp. 912–922, 2017.
B. Patel, V. Kakuste, and M. Eirinaki, “CaPaR: A career path recommendation framework,” Proceedings - 3rd IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2017, pp. 23–30, 2017.
N. Rajganesh, S. Seetha Devi, J. Keerthana, and R. Poovizhi, “A Personalized Job Recommended System Using Hybrid Collaborative Filtering Algorithm,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology © 2018 IJSRCSEIT, vol. 3, no. 11, pp. 191–196, 2018.
A. Gupta and D. Garg, “Applying data mining techniques in job recommender system for considering candidate job preferences,” Proceedings of the 2014 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2014, pp. 1458–1465, 2014.
B. Heap, A. Krzywicki, W. Wobcke, M. Bain, and P. Compton, “Combining Career Progression and Profile Matching in a Job Recommender System,” PRICAI, pp. 396–408, 2014.
I. A. Heggo and N. Abdelbaki, “Hybrid Information Filtering Engine for Personalized Job Recommender System,” Advances in Intelligent Systems and Computing, vol. 723, pp. 553–563, 2018.
S. Yang, M. Korayem, K. AlJadda, T. Grainger, and S. Natarajan, “Combining content-based and collaborative filtering for job recommendation system: A cost-sensitive Statistical Relational Learning approach,” Knowledge-Based Systems, vol. 136, pp. 37–45, 2017.
D. Meira, J. Viterbo, and F. Bernardini, “An experimental analysis on scalable implementations of the alternating least squares algorithm,” Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, FedCSIS 2018, vol. 15, no. i, pp. 351–359, 2018.
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