Job Recommendation System using LinkedIn User Profiles

One of the services that LinkedIn offers is a job recommendation system that helps users find job opportunities that match their interests and skills. This paper investigates the recommender system for LinkedIn user profiles to generate job recommendations, highlighting the key factors and approaches that influence the recommendation process and there is potential for bias in LinkedIn's job recommendation system. The content-based filtering approach involves using the attributes of the job posting and the user's profile to generate job recommendations. Collaborative filtering is an approach that uses the behaviour of other users to generate recommendations. This could involve introducing demographic data into the recommendation process or using more advanced machine learning techniques to eliminate bias. The findings can be used to inform future improvements to the job recommendation service, making it more effective for users and promoting more equitable hiring practices. By improving the job recommendation system, LinkedIn can provide greater value to its users and support the growth of professional networks and job opportunities.