Employment Recommendation System using Matching, Collaborative Filtering and Content Based Recommendation

Abstract

Abstract:The tremendous growth of both information and usage has led to a so-called information overload problem in which users are finding it increasingly difficult to locate the right information at the right time Thus huge amount of information and easy access to it make recommender systems unavoidable [1]. We use recommender system every day without realizing it and without knowing what exactly happens. Recommender systems have changed the way people find products, information, and even other people. They study patterns of behavior to know what someone will prefer from among a collection of things he/she has never experienced. Benefits of recommender systems to the businesses using them include: The ability to offer unique personalized service for the customer, Increase trust and customer loyalty, Increase sales, click-through rates, conversions, etc., Opportunities for promotion, persuasion and Obtain more knowledge about customers. Recommender systems are software tools and techniques providing suggestions for items to be of use to a user. 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 [2] exist that use different strategies, here efforts have been put to make the job recommendations on the basis of candidates profile matching as well as preserving candidates job behavior or preferences. Firstly, the rules predicting the general preferences of the different user groups are mined. Then the job recommendations to the target candidate are made on the basis of content based matching as well as candidate preferences, which are preserved either in the form of mined rules or obtained by candidates own applied job history.

Technical Job Recommendation System Using APIs and Web Crawling

“…Pradhan et al reveal a comparison between exploring relations amid known features and things describing items [ 9 ]. A system to make the proper recommendations based on candidates' profile matching as well as saving candidates' job preferences has been proposed in [ 10 ]. Here, mining is done for the rules predicting the general activities.…”

“…Here, mining is done for the rules predicting the general activities. Then, recommendations are made to the target candidate based on content-based matching and candidate preferences [ 10 ]. Manjare et al proposed a specific model (CBF or content-based filtering) and social interaction to increase the relevance of job recommendations.…”

A Recommendation-Based Contextual Model for Talent Acquisition

“…Highly trained models were used to predict the ranking and sorting of resumes (Kumar et al, 2017). Belsare and Deshmukh (2018) presented the employment recommendation system, which uses various recommendation methods. Which are simple matching, collaborative and content-based filtering Belsare and Deshmukh (2018).…”

Support potential of elite civil universities for China’s space industry: Higher educational mobilization capacity for China’s space ambition

“…A cosine similarity measure represents the similarity between two vectors of an inner product space, which is often used to measure similarity in documents for text analysis [ 40 ]. Some scholars have used the method in employment analysis to correlate jobs and people [ 41 , 42 ], to which this study referred. Each university’s HRPS was calculated for each discipline by counting a university’s enrollment plan for bachelor’s, master’s, and doctorate programs, which were weighted as 1, 2, and 3, respectively.…”