-  The smart network where hbo and wo students find their internship and first job.

The smart network where hbo and wo students find their internship and first job.

Data Science - BSc/MSc thesis

Posted 1 Feb 2024
Work experience
0 to 1 years
Full-time / part-time
Job function
Degree level
Required language
English (Fluent)

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Review state of the art Recommendation Algorithms at

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 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


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, and your university supervisor.

  • Literature Review: Conduct an in-depth literature review of existing recommender system algorithms, focusing on their applications in job recommendation domains.
  • Algorithmic Comparison: Compare and contrast the performance, strengths, and weaknesses of prominent recommender system algorithms, including collaborative filtering, content-based filtering, matrix factorization, and hybrid methods.
  • Contextual Adaptation: Investigate the adaptability of these algorithms to the unique characteristics of job recommendation scenarios, considering factors such as sparse data, evolving user preferences, and the dynamic nature of job postings.
  • Evaluation Metrics: Establish relevant evaluation metrics to assess the effectiveness and efficiency of each algorithm in the context of's job recommendation platform.
  • Algorithm Implementation: Implement selected recommender system algorithms within the environment, customizing and fine-tuning parameters to optimize performance.
  • User Feedback Analysis: Collect and analyze user feedback to gauge the real-world effectiveness of the implemented algorithms. Understand user satisfaction, engagement, and the impact on successful job matches.

Expected Outcomes

  • A comprehensive understanding of the strengths and limitations of various recommender system algorithms in the context of job recommendations.
  • Insights into the adaptability of these algorithms to the unique challenges posed by's platform and user base.
  • Practical recommendations for enhancing the efficiency and accuracy of job recommendation algorithms on
  • (optionally) A validated and optimized recommender system implementation within the ecosystem.


This thesis contributes to the optimization of'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's platform, and both quantitative and qualitative analysis methods will be employed to draw meaningful conclusions.


  • are a Bachelor or Master student with a relevant study background (Data Science or comparable)
  • are looking for a graduate internship in Recommender Systems
  • want to gain practical experience in a dynamic company
  • want your work to have a real impact!

Practical info

  • works hybrid, so partially working from home is possible. Interviews and initial project setup will be in-person at our Rotterdam office.
  • Start- and end dates will be determined in collaboration
  •'s recommender systems are built in Python (with sklearn, pandas) and use the Google Cloud Platform.
  • Earliest start date is 2024-04-01, but a later kickoff is possible. is the #1 career network for students, graduates and employers in the Netherlands. 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 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.

Active in 2 countries
33 employees
70% men - 30% women
Average age is 27 years

What employees are saying

Bette Donker

Data Analyst

Bette Donker

I really like that everyone at 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!

Mike Rovroy

Product Designer UX/UI

Mike Rovroy

What I love about, 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.