Machine Learning based Candidate Recommendation System using Bayesian Model
- Title
- Machine Learning based Candidate Recommendation System using Bayesian Model
- Creator
- Manuval G.G.; George T.T.; Aby B.P.; Mathew M.; Chandran A.S.; Jayapandian N.
- Description
- Online websites that recommend books, music, movies, and other media are becoming increasingly prevalent because of collaborative filtering. This online websites are using many algorithms to provide the better recommendation to attract the customer. Bayesian statistics, which is based on Bayes' theorem, is a technique for data analysis in which observable data are used to update the parameters of a statistical model. To discuss a strategy called item-based collaborative filtering, which bases predictions on the similarities between the said objects. This uses Machine Learning based Candidate Recommendation System which uses Bayesian Model database to assess the proposed method. The actual results show that for collaborative filtering which is based on correlation, the Bayesian techniques we have proposed outperform traditional algorithms. Also discuss a technique for improving prediction accuracy that combines the Simple Bayesian Classifier with user- and item-based collaborative filtering. The user-based recommendation is then applied to the matrix once the user-item rating matrix has been filled out with pseudo-scores produced by the item-based filter. This model is demonstrated that the combined approach outperforms the individual collaborative recommendation approach. The creation of UI based web application will help Students to manage achievement details. Job seekers and admin will be given a separately formatted version of the application where, students can upload and view their certificate, wherein admin can access student achievement details categorized by different parameters. This proposed model is developed under the service learning scheme to benefit both job seeker and recruiter. 2023 IEEE.
- Source
- Proceedings of the 2023 2nd International Conference on Electronics and Renewable Systems, ICEARS 2023, pp. 1172-1178.
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Artificial Intelligence; Bayesian Technique; Decision Tree; Job Seeker; Machine Learning; Service Learning
- Coverage
- Manuval G.G., Christ (Deemed to Be University), Department of CSE, India; George T.T., Christ (Deemed to Be University), Department of CSE, India; Aby B.P., Christ (Deemed to Be University), Department of CSE, India; Mathew M., Christ (Deemed to Be University), Department of CSE, India; Chandran A.S., Christ (Deemed to Be University), Department of CSE, India; Jayapandian N., Christ (Deemed to Be University), Department of CSE, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835034664-0
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Manuval G.G.; George T.T.; Aby B.P.; Mathew M.; Chandran A.S.; Jayapandian N., “Machine Learning based Candidate Recommendation System using Bayesian Model,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19984.