`ImplicitALSWrapperModel` support for incremental training in `fit_partial`
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
- Issue type
- Feature
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- python
- Domain
- machine-learning
Research direction
Read the discussion in issue #176 and the prerequisite work in issue #240 before assessing this feature. The issue does not name implementation files or tests; first trace ImplicitALSWrapperModel and its fit_partial entry point. Done means fitted models can accept new users, items, and interactions from the supplied dataset for incremental training.
Written by the indexing model from the issue text.
Description
Feature Description
Fitted model support to accept new users, items and interactions from dataset that is passed to fit_partial method.
Why this feature?
Allows incremental training
Additional context
Discussed here: https://github.com/MobileTeleSystems/RecTools/issues/176
Updating dataset with new data should be implemented first: https://github.com/MobileTeleSystems/RecTools/issues/240
- Dominant language
- Python
- Stars
- 457
- Forks
- 55
- PR merge metrics
- No merged PRs in 30d
Getting set up
- No Dockerfile or Docker Compose file
- Has a pull request template
- Read the contributing 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.
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