Class-oriented document vectorization as a new vectorization paradigm: application to job recommender systems

Nowadays, job recommender systems are more useful in the fight against unemployment due to their strong presence in e-recruitment platforms that are becoming very popular. Most of the job recommender systems based on machine learning models use a vector representation of job offers based on keywords. However, these keywords are results of vectorization which is applied on a collection of documents where each one is a job offer. In this case, each keyword discriminates one job offer from another, whereas it can be preferable that each keyword discriminates one class from another. Our aims is to improve job recommendation to user profiles, by applying vectorization on a class-oriented collection of documents in order to obtain more useful keywords for job offer representation. In this context, each class-oriented document corresponds to a user profile. Experiments are done on three datasets (Monster, Nigam and Minajobs), using TF-IDF and Doc2Vec as vectorization techniques, Naive Bayes, Decision Trees, Support Vector Machine (SVM), and Tranformers erchitecture (TFM) as machine learning models for top-N recommendation, and Precision, MAP and F1-score as evaluation metrics. Results show that, compared to classic job recommender systems, improvement rates can go up to 19\%, 22\% and 20\% for systems based on Naive Bayes, go up to 34\%, 37\% and 34\% for those based on Decision tree, go up to 33\%, 34\%, 34\% for those based on SVM, and go up to 29\%, 40\% and 33\% for those based on transformers architecture, respectively in the Monster, Nigam and Minajobs datasets.