Tutorial on deployment of lightfm model to production

Nobody has claimed this yet.

It is just a feature request and not a bug or issue.

Could you guys please include a simple example of creating a server(maybe using Flask) that can be used for recommending items to users?

In the paper "Metadata Embeddings for User and Item Cold-start Recommendations" there is a paragraph on handling new users in an online manner.

```Thousands of new items and users appear on Lyst everyday. To cope with this, we train our LightFM model in an online manner, continually updating the representations of existing features and creating fresh representations for features that we have never observed before.```

Will it be possible to include a simple tutorial on that too using open source data like MovieLens? I believe it will really help out the community.

Thank you.

Start by reviewing the existing LightFM tutorials and documentation structure, then outline the requested Flask-style production server example and the online update workflow. Use MovieLens as the example dataset; done means the tutorial covers deployment, recommendations for users, and handling new users and items.

Written by the indexing model from the issue text.