Hybrid Recommender: Adding new users and items to an existing model
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- Dominant language
- Python
- Stars
- 5.1k
- Forks
- 724
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Description
Does LightFM support a way to initialize and add embeddings for new users and items to an existing (pre-trained) model, so that we don't lose all the pretrained embeddings and also being able to index those new users and items, without having to build and train a completely new model from scratch.
Contributor guide
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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
The issue does not identify files, tests, or specific entry points. Start by examining LightFM's existing pre-trained model and user/item indexing APIs, then determine how new embeddings could be initialized without discarding existing ones. Done means new users and items can be indexed while preserving the pre-trained embeddings and avoiding full retraining.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100