Add folding-in support to SKL SVD
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components
- Dominant language
- Python
- Stars
- 314
- Forks
- 77
- Avg merge
- 4d 6m
- Merged PRs (30d)
- 10
Description
We should add folding-in support to the SciKit-Learn SVD. That is, given the user's ratings at __call__ time, we need to normalize them and multiply them by the item matrix.
- Add an option for how to handle user preferences in the configuration, like we have for
als.BiasedMFScorer - Respect this option in
__call__to compute a new user embedding vector
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First steps
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- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start at the SciKit-Learn SVD implementation and its call method, then compare its configuration with als.BiasedMFScorer. Trace how the item matrix and user ratings are represented and normalized. Done means a configurable user-preference option is respected by call to compute a new user embedding for folding-in.
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
- 45/100