Job recommender systems are desired to attain a high level of accuracy while making the predictions which are relevant to the customer, as it becomes a very tedious task to explore thousands of jobs, posted on the web, periodically. Although a lot of job recommender systems exist that use different strategies, here efforts have been put to make the job recommendations on the basis of candidate‟s profile matching as well as preserving candidate‟s job behavior or preferences. We will make use of 3 different models to recommend the job. The algorithms we will be using are Navie Bayes, Logistic Regression and Random Forest. Through this technique, a significant level of accuracy, around eighty percent, has been achieved over other basic methods of job recommendations.
1. Gaussian Naïve Bayes
2. Logistic Regression
3. Neural Network
- Job Recommendation System Reference Paper 00:00:00
- Job Recommendation System Synopsis 00:00:00