Negative labels in Ranking model

I have a dataset consisting of users positive interactions with assets. Before I was using a Retrieval Model, whose objective considers as positive label a given user-asset pair, and as negatives the user paired with the rest of the assets in the batch. But I have to move to a Ranking model due to non static dataset requirements.

However, the ranking model objective does not do that. Instead, it requires a target label for each pair. Given my dataset (and following the movies example) I should fill that tensor with 1s. But then, I wouldn't have negative examples, and the model would end up predicting 1s for all datapoints.

Is there a simple way to use in a Ranking model the rest of elements within the batch as negatives? What is the best setup to train a Ranking model with this king of data?

Thanks