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In the dynamic landscape of talent acquisition, recommender systems play a pivotal role in connecting job seekers with a job they love; the goal of Magnet.me. This thesis aims to conduct a comprehensive review and comparative analysis of state-of-the-art recommender system algorithms, specifically tailored to the context of job recommendations at Magnet.me.
Depending on the duration and nature of the thesis project, a (sub)selection of these topics will be relevant. The objective will be set in collaboration with Magnet.me, and your university supervisor.
This thesis contributes to the optimization of Magnet.me's job recommendation system, fostering more precise and satisfying job matches for users. By leveraging the latest advancements in recommender system algorithms, the study aims to enhance the overall user experience, drive user engagement, and ultimately improve the success rate of job placements.
The research will employ a combination of literature review, algorithmic implementation, and user feedback analysis. Data will be collected from Magnet.me's platform, and both quantitative and qualitative analysis methods will be employed to draw meaningful conclusions.
Magnet.me is the #1 career network for students, graduates and employers in the Netherlands. Magnet.me uses AI to empower every student to build a network of interested employers and to discover jobs based on their profile and preferences. Companies use Magnet.me to establish meaningful connections with young talent and to fill their internships and jobs without hassle. More than 300,000 students and 5,000 companies, from multinationals to startups, are currently on the network.
I really like that everyone at Magnet.me is very open and works hard towards a common ambitious goal. As a Data Analyst, I monitor our marketplace to provide insightful data for informed decisions. This role challenges me both strategically and technically, which stimulates me to keep on learning!
What I love about Magnet.me, is its versatility. We have a great mix of creative marketeers, a helpful support squad, typical Delft developers, and customer-oriented sales people. Everyone adds something, especially to the great atmosphere during Friday afternoon drinks.