Job Recommendation System using Content and Collaborative Filtering Based Techniques
Juhi Dhameliya1, Nikita Desai2
1Juhi Dhameliya*, Department of Information Technology, Dharmsinh Desai University, Nadiad, India.
2Nikita Desai, Department of Information Technology, Dharmsinh Desai University, Nadiad, India.
Manuscript received on September 02, 2019. | Revised Manuscript received on September 05, 2019. | Manuscript published on September 30, 2019. | PP: 8-13 | Volume-9 Issue-3, September 2019. | Retrieval Number: C3266099319/19©BEIESP | DOI: 10.35940/ijsce.C3266.099319
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Internet based recruiting platforms decrease advertisement cost, but they suffer from information overload problem. The job recommendation systems (JRS) have achieved success in e-recruitment process but still they are not able to capture the complexity of matching between candidates’ desires and organizations’ requirements. Thus, we propose a hybrid JRS which combines recommendations of content-based filtering (CBF) and collaborative filtering (CF) to overcome their individual major shortcomings namely overspecialization and over-fitting. In proposed system, CBF model makes recommendations based on candidates’ skills identified from past jobs in which they have applied and CF model makes recommendations based on jobs in which similar users have applied and also those jobs in which that user has applied frequently together in very similar contexts using Word2Vec’s skip-gram model. We used k-Nearest Neighbors technique and Pearson Correlation Coefficient. The recall of our proposed model is found to be 63.97% on a data set which had nearly 1900+ jobs and 23,000 job applicants.
Keywords: Collaborative Filtering (CF), Content Based Filtering (CBF), Hybrid Approach, Information Filtering, Job Recommendation System (JRS), Knowledge based Approach, Word2Vec models.