Implementing Incremental Training and Feedback loop using Lightfm

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I have successfully trained a lightfm model based on user features, item features and interactions. Now I want to incrementally train the model to cover two aspects:
1. Addition of new items/users and their interactions
2. Update model based on user feedback(preferably both positive and negative) of the provided recommendations

For the first case, I can see the `fit_partial` function in the [Dataset](https://making.lyst.com/lightfm/docs/lightfm.data.html#lightfm.data.Dataset.fit_partial) and [Model](https://making.lyst.com/lightfm/docs/lightfm.html#lightfm.LightFM.fit_partial) which seem to be the way, but I'm not sure.
For the second case, I couldn't find anything in the documentation, so I am not sure how to proceed.
Any directions would be appreciated

Start with the Dataset.fit_partial and LightFM.fit_partial documentation linked in the issue, then inspect how they address new users, items, and interactions. Determine whether feedback-based updates, including negative feedback, are supported or require a defined API change. Done should clearly document the supported incremental-training workflow and feedback behavior.

Written by the indexing model from the issue text.