Abstract
Managing huge measure of enlisting data on the web, a job seekers dependably invests hours to find helpful ones. To decrease this relentless work, we structure and actualize a recommendation system for online job-seeking job recommender systems are wanted to achieve an uncommon state of precision while making the rating predicts which are significant to the client, as it turns into a repetitive assignment to review a huge number of jobs, posted on the web for instance LinkedIn, fresherworld.com, naukri.com and so on intermittently. In spite of the fact that a great deal of job recommender systems exist that utilization various techniques, here undertaking have been put to make the job recommendations based on applicants profile coordinating just as safeguarding applicants job conduct or inclinations. The collaborating filtering contains a list of rating that the previous user has already given for an item. This paper shows a concise review of collaborative filtering rating prediction based job recommender system and their execution utilizing RapidMiner.
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References
Ramezani, M., Bergman, L., Thompson, R., Burke, R., Mobasher, B.: Selecting and applying recommendation technology. In: Proceedings of International Workshop on Recommendation and Collaboration in Conjunction with International ACM on Intelligence User Interface (2008)
Rafter, R., Bradley, K., Smyth, B.: Automated collaborative filtering applications for online recruitment services. In: Adaptive Hypermedia and Adaptive Web-Based Systems. Lecture Notes in Computer Science, vol. 1892, pp. 363–368 (2000)
Balabanovic, M., Shoham, Y.: Conent–based collaborative recommendation. Commun. ACM 40(3), 66–72 (1997)
Ha-Thuc, V., Xu, Y., Kanduri, S.P., Wu, X., Dialani, V., Yan, Y., Gupta, A., Sinha, S.: Search by Ideal Candidates: Next Generation of Talent Search at LinkedIn (2016). https://doi.org/10.1145/2872518.2890549
Hayes, C., Cunningham, P.: Smart radio—community based music radio. Knowl. Based Syst. 14 (2001)
Belsare, R.G., Deshmukh, V.M.: Employment Recommendation System using Matching Collaborative Filtering and Content Based Recommendation
Wang, Q., Yuan, X., Sun, M.: Collaborative Filtering Recommendation Algorithm based on Hybrid User Model. FSKD (2010)
Pazzani, M.J., Billsus, D.: Content-based recommendation systems. The Adaptive Web. Springer Berlin Heidelberg, pp. 325–341 (2007)
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). Jacobs, I.S., Bean, C.P.: Fine particles, thin films and exchange anisotropy. In: Rado, G.T., Suhl, H. (eds.) Magnetism, vol. III. Academic, New York, pp. 271–350 (1963)
Schafer, J.B., Frankowski, D., Herlocker, J., et al.: Collaborative filtering recommender systems. The Adaptive Web. Springer Berlin Heidelberg (2007)
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. ACM (2001)
Wei, K., Huang, J., Fu, S.: A survey of e-commerce recommender systems. In: International Conference on Service Systems and Service Management, pp. 1–5, June 2007
Zhang, C., Cheng, X.: An ensemble method for job recommender systems. In: Recommender Systems Challenge’16, Boston, MA, USA 2016 ACM, Sept. 2016
Jain, A., Vishwakarma, S.K.: Collaborating filtering for movie recommendation using RapidMiner. Int. J. Comput. Appl. 169 (2017)
Sarwar, B., Karypis, G., Konstan, J.A., Riedl, J.: ItemBased collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference of World Wide Web, pp. 285–295 (2001)
De Pessemier, T., Vanhecke, K., Martens, L.: A scalable, high-performance algorithm for hybrid job recommendations. In: Proceedings of the Recommender Systems Challenge (RecSys Challenge’16). ACM, New York, NY, USA, Article 5, 4 pp. (2016)
Zhang, Y., Yang, C., Niu, Z.: A research of job recommendation system based on collaborative filtering. In: International Symposium on Computational Intelligence and Design (2014)
Miheleie, M., Antulov-Fantulin, N., Bosnjak, M., Smuc, T.: e-LICO: An e-Laboratory for Interdisciplinary Collaborative Research in Data Mining and Data—Intensive Science by the European Community 7th Framework ICT (2007)
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Patel, R., Vishwakarma, S.K. (2020). An Efficient Approach for Job Recommendation System Based on Collaborative Filtering. In: Tuba, M., Akashe, S., Joshi, A. (eds) ICT Systems and Sustainability. Advances in Intelligent Systems and Computing, vol 1077. Springer, Singapore. https://doi.org/10.1007/978-981-15-0936-0_16
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DOI: https://doi.org/10.1007/978-981-15-0936-0_16
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