What's a good way to "weight" the importance of item/user features in LightFM?

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Hey all, I have been using LightFM in an e-commerce application with a decent amount success. Thanks for this really cool package!

I'm in the process of optimizing the recommendation outputs. Because of specific business domain considerations, certain features are "more important" and have less leeway in terms of variability than others, like "price" (i.e. certain recommended items need to be in a soft price range).

From other posts, I have seen that the best way to deal with a numerical item feature like "price" is to discretize it. I have so far used decile bins. **Is there also a way for LightFM to weight this feature MORE than the others?**.

Thanks in advance!

Edit: I also added this question in [stackoverflow](https://stackoverflow.com/questions/62221190/whats-a-good-way-to-weight-the-importance-of-item-user-features-in-lightfm)

The issue provides no file, test, or entry point to inspect. Start by reviewing LightFM's feature representation and training documentation to determine whether feature weighting is supported, then document the supported approach or define the required behavior and validation criteria.

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