Overview
What this challenge is about.
Knowledge-Graph Recommender for a Lisbon Bookstore. Intermediate challenge in code. Writing production code that solves real engineering problems, earn a blo...
The Brief
What you'll do, and what you'll demonstrate.
Can a rules-plus-similarity recommender built over a catalog knowledge graph match the team's hand-curated staff-pick quality, measured as precision-at-10 within 15 percent, without using any cross-customer purchase history?
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
Earning criteria — what you'll demonstrate
- Represent a literary catalog as a knowledge graph with clear node types and edge semantics.
- Combine symbolic traversal rules with numeric similarity scoring into a single ranked recommendation.
- Design and run a precision-at-10 evaluation against a curated baseline using a held-out split.
- Translate a working service into an integration spec a frontend team can build against unaided.
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Knowledge Representation
Apply knowledge representation to solve real industry problems and demonstrate production-level capability.
- Knowledge Graphs
Apply knowledge graphs to solve real industry problems and demonstrate production-level capability.
- Python Programming
Apply python programming to solve real industry problems and demonstrate production-level capability.
- Algorithm Evaluation
Apply algorithm evaluation to solve real industry problems and demonstrate production-level capability.
- Recommender Systems
Apply recommender systems to solve real industry problems and demonstrate production-level capability.
- Intelligent Agents
Apply intelligent agents to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Recommender Systems Engineer
This challenge mirrors real recommender work: turning rich item metadata into a ranked, explainable recommendation and proving its quality against a baseline. You leave with a portfolio piece showing you can ship a content-based recommender end to end.
This challenge sharpens
- recommender-systems
- knowledge-graphs
- algorithm-evaluation
Knowledge Engineer
Modeling a domain as nodes, edges, and traversal rules is the core of knowledge engineering. By designing a clean schema and reasoning over it, you practice the representation and rule-design skills these roles depend on daily.
This challenge sharpens
- knowledge-representation
- knowledge-graphs
- intelligent-agents
Backend Engineer (Search & Discovery)
Discovery teams need backend engineers who can build a service, score results, and hand a clean integration spec to frontend. This challenge exercises exactly that path from data model to deployable service.
This challenge sharpens
- python-programming
- recommender-systems
- algorithm-evaluation