Recommender Systems
If you like applying Recommender Systems, every challenge here gives you a chance to practice it on a real industry brief.
- ResearchAdvancedNew
Evaluate a Knowledge-Graph-Augmented Recommender
You receive permission to use the public MovieLens 1M dataset plus a derived item-KG (movie -> genre, director, decade) built from Wikidata. Train two recommenders: a matrix-fac…
- Knowledge Graph Embeddings
- Recommender Systems
- Benchmarking
Knowledge Graphs and Semantic Web - CodeAdvancedNew
Build a Hybrid Recommender for a Niche Consumer-AI Music App
You receive listening events (around 240 million plays) plus a content embedding per track (audio + curator tags). Build a collaborative filtering model (ALS or implicit-feedbac…
- Recommender Systems
- Collaborative Filtering
- Content Based Filtering
Data Mining and Knowledge Discovery
How it works
From brief to credential, in six steps.
Step 01
Browse challenges aligned to your studies.
Step 02
Accept the one that fits your goals.
Step 03
Work through it with AI Copilot guidance.
Step 04
Submit for structured evaluation.
Step 05
Earn a verified credential.
Step 06
Add it to LinkedIn with one click.
Industry teams behind a decade of practitioner briefs
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Industry teams can shape briefs around the skills they hire for, then evaluate students on rubric-scored deliverables — not resumes.