How to handle very popular items?

Hello! Thanks for making this great framework available.

In my scenario, I have user-item interaction data which I use to create implicit ratings, currently without user or item features. The user-item interaction data is very sparse and there are also some items that are extremely popular relative to others, so the predictions generally look like a list of most popular items.

So I am wondering what approach would you suggest to create more diversity and long-tail items in the predictions?

It seems to me a naive approach could be to weight the implicit ratings relative to the global popularity of the item. But maybe including user and item features circumvents this whole popularity bias problem?

Thanks in advance!

This issue names no files, tests, or entry points. Start by reading the 21-comment discussion to understand whether the desired direction is popularity weighting, user and item features, or another approach. Done would require an agreed, actionable scope for improving recommendation diversity and long-tail results.

Written by the indexing model from the issue text.