Seeking Guidance on Feature Preparation for Retrieval and Ranking Models

Hello,

I am currently working on developing a recommender system for internal customers and have found this framework to be a great tool to start with. As a beginner in the field of recommender systems, I am hoping to learn from the experts here.

The current problem I am facing involves a relatively small dataset of around 100 users and 200k items. However, this item dataset is expected to increase to 4 million in the near future. I have binary interaction data as well as negative labels (items presented to users that they did not act on), although the size of the negative labels is small.

Based on my research, a two-stage or multi-stage recommender system seems to be a better approach compared to a single-stage recommender. Additionally, from the other discussions here, it seems that it is a default practice to train the retrieval model with positive data points and the ranking model with both positive and negative data points.

The remaining doubts I have are

1. How to prepare features for the retrieval model and the ranking model, respectively? Should they be the same or different?
2. Is sparsity in the interaction matrix a significant issue? In this problem setup, items have to be interacted with at least once by a user, but typically by a few users only at most due to the ownership mechanism in place (a user has to give up an item so that other users can own it).

Any guidance or suggestions would be greatly appreciated. Thank you!

No files, tests, or entry points are named. First clarify the intended deliverable, then investigate feature preparation for retrieval and ranking models and the impact of sparse interactions at the stated dataset sizes. Done would require concrete, project-specific guidance with an agreed scope.