When creating embedding tables for users and items, what size should you expect for the user embedding table?

The correct answer is based on the relationship between the user embedding table size and the number of users in the system. When creating an embedding table for users, each user requires a unique vector representation, capturing their characteristics and preferences. Therefore, the size of the user embedding table should be proportional to the number of users. Each user typically has their own embedding that contributes to the overall table size, ensuring that there’s a dedicated representation for each one.

This approach allows the model to learn specific user preferences from the data. If the embedding table were not proportional to the number of users, you could potentially end up with either redundant representations or an insufficient number of embeddings to adequately represent all users, which would hinder model performance.

The other options do not accurately reflect this need. The size being proportional to the number of items would not serve the purpose of representing users because the user characteristics are not directly linked to item numbers. A fixed size for all users would underestimate or overestimate the actual representation needed, failing to account for differences among user behaviors. Similarly, making the size dependent on user interaction frequency doesn’t address the fundamental requirement of having a unique embedding for every user; while interaction frequency can influence how often embeddings are updated, it should not limit the number of