MLBazaar / MLBazaar/MLPrimitives
Integrate surprise for recommender systems/collaborative filtering
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 70
- Forks
- 37
- PR merge metrics
- No merged PRs in 30d
Description
Surprise provides a variety of solutions for recommender systems. It has SVD, SVD++, NMF, Slope One, k-NN, Centered k-NN, k-NN Baseline, Co-Clustering, and Random. It is pretty close to scikit-learn API so perhaps we only need JSONs to add them as primitives.
https://github.com/NicolasHug/Surprise
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing how MLPrimitives represents primitives as JSONs and compare those definitions with Surprise's documented recommender algorithms and scikit-learn-like API. Done means the requested Surprise solutions are represented as usable primitives with the necessary configuration details; the issue does not name specific files or tests.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, scikit-learn
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100