Recommender Systems Engineering Certification

The Recommender Systems Engineering Certification is a free advanced-level, 25-minute skill test from Science of Apps. It tests recommender systems engineering — collaborative filtering and matrix factorisation, two-tower candidate retrieval, ranking models such as Wide and Deep, sequential and transformer recommenders, cold start and implicit feedback, exploration with contextual bandits, offline metrics and online A/B testing, and responsible recommendation covering feedback loops, diversity, fairness and the EU Digital Services Act — written to the YouTube DNN, matrix factorisation, two-tower, SASRec and LinUCB papers for 2026 and 2027. Score 70% or more on its 25 questions to earn a verifiable Recommender Systems Engineering certificate (SOA-RSE). Every question is based on published standards and research — including Matrix Factorization Techniques for Recommender Systems (Koren, Bell and Volinsky, IEEE Computer (2009)), Deep Neural Networks for YouTube Recommendations (Covington, Adams and Sargin (Google Research)), A Contextual-Bandit Approach to Personalized News Article Recommendation (Li et al., 2010) (arXiv) — and each answer is explained with its source.

Science of Apps offers a free Recommender Systems Engineering Certification, written for 2026 and 2027. It tests collaborative filtering foundations, retrieval and ranking architectures, cold start and exploration, evaluation and experimentation and responsible recommendation with 25 original scenario questions in all, each naming its published source; you get an instant score and a verifiable certificate with a credential ID you can add to LinkedIn.

Read the five skill areas on this page, then take the 25-minute test online — no fee, course or sign-up. You see every answer explained when you finish. Score 70% or more and you get a Recommender Systems Engineering certificate straight away, on screen, as a PDF and by email, with a credential ID anyone can verify.

Yes. The questions are original scenarios written by Science of Apps, and each one is based on a public source you can read: Matrix Factorization Techniques for Recommender Systems (Koren, Bell and Volinsky, IEEE Computer (2009)); Deep Neural Networks for YouTube Recommendations (Covington, Adams and Sargin (Google Research)); A Contextual-Bandit Approach to Personalized News Article Recommendation (Li et al., 2010) (arXiv); Introduction to Information Retrieval (Manning, Raghavan and Schütze (Cambridge University Press)); Unbiased Learning-to-Rank with Biased Feedback (Joachims et al., 2017) (arXiv); Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations (Yi et al., 2019) (Google Research). The source is shown under each question and linked in the answer review. Science of Apps doesn't copy questions from other tests.

Five skill areas with five questions each, drawn from a bank of 10 per area so a retake brings new questions: Collaborative Filtering Foundations (Matrix factorisation, Implicit feedback and Neighbourhood methods); Retrieval and Ranking Architectures (Two-tower retrieval, Ranking models and Sequential recommenders); Cold Start and Exploration (New users and items, Content features and Contextual bandits); Evaluation and Experimentation (Offline metrics, Online A/B tests and Long-term effects); Responsible Recommendation (Feedback loops, Fairness and diversity and Regulation and transparency). Every question is an original scenario written to the 2026–2027 standard, with the published source named under it.

Recommender systems or ranking engineer, ML engineer for personalisation, Data scientist for growth or feeds, Applied scientist in e-commerce or media and Product manager for recommendations. It suits students at leading universities, career switchers and experienced professionals who want quick, verifiable proof of Recommender Systems Engineering knowledge in 2026 and 2027.

CS and AI students at MIT, Stanford, CMU, Berkeley, IITs, Tsinghua and ETH are hired on depth beyond tool use: model internals, systems and hardware. Passing earns a founder-signed Science of Apps certificate with a credential ID and QR code that recruiters can verify.

Large systems usually retrieve a few hundred candidates from millions of items with fast models such as two-tower embeddings and approximate nearest neighbour search, then rank those candidates with a heavier model that uses many features, and finally apply business rules and diversity re-ranking.

They use content features, metadata and context, popularity or editorial defaults, onboarding questions and exploration strategies such as bandits until enough interaction data is collected.

Yes. The test is online, in English and free everywhere — people take it from India, the US, the UK, Europe, Africa, Latin America and Asia-Pacific. Your certificate is dated with your own local date and can be verified from anywhere at scienceofapps.com/certification/verify/.

25 questions — 5 from each of 5 skill areas — with a 25-minute limit. You need 70% (18 of 25) to pass and 90% for distinction.

We rate it Advanced: assumes years in the role or deep specialist knowledge. It has 25 scenario questions — 5 from each of 5 skill areas — and a 25-minute limit. You need 70% (18 of 25) to pass and 90% for distinction.

Nothing. There is no fee to take a test, to receive the certificate, to have it emailed or to verify it, and no sign-up.

On our server, not in your browser. The answers never reach the page until you submit, so a certificate can only be earned by passing. Each certificate gets a unique credential ID stored by Science of Apps.

You see your score for each area and every question with the correct answer and why, with the published source behind it. You can retake after 30 minutes, and the retake draws questions you haven't seen yet.

No. Every certificate is dated, so it always shows when you passed. Retake a test any time for a newer date or a higher score.

Yes. After you pass, Add to LinkedIn opens the Licenses & certifications form with the certificate name, Science of Apps as the issuer, the issue date, your credential ID and the verification link filled in. You can also download it as a PDF or PNG, and it's emailed to you as a PDF.

Every certificate has a credential ID and a QR code. Scanning it, or entering the ID at scienceofapps.com/certification/verify/, shows the holder's name, the certification, the score and the issue date from our records.

No. They are Science of Apps' own assessments of applied skill. They aren't issued by a university or standards body, and they work best alongside a portfolio that shows the skills in real work.