Item similarity without using `interactions` data?
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- Dominant language
- Python
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Description
Not an issue, just a question. In the `fit` method I see that `user_features` and `item_features` are optional.
```
fit(interactions, user_features=None, item_features=None, sample_weight=None, epochs=1, num_threads=1, verbose=False)
```
Wondering for a totally cold start problem where all I have are `item_features` if it is possible to fit a model, e.g. something like:
```
fit(item_features, interactions=None, user_features=None...)
```
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
Start with the fit method signature and its handling of interactions, user_features, and item_features. Determine whether fitting from item_features alone is supported; done should be a documented answer or a clearly scoped implementation plan for cold-start training.
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
- 25/100