LightFM中何时需将preserve_rows参数设置为True？

Great question! Let's walk through the key scenarios where you'll want to toggle this parameter to True:

• You need evaluation results aligned with your full user list
  If you plan to join the precision scores back with your original user dataset (for example, to analyze how precision correlates with user demographics, or to attach scores to user profiles), you can't afford to drop any users. Setting preserve_rows=True ensures every user from your test set is included in the output—even those with no positive samples in their test split (their precision will be 0).

• You want an accurate global average metric
  By default, preserve_rows=False filters out users with no positive test samples, which means your average precision score will only reflect users who had at least one interaction in the test set. If you need a true population-level average that includes all users (including those with zero positive test interactions, who contribute a 0 to the average), you must set preserve_rows=True. Otherwise, your average will be artificially inflated.

• Downstream tasks require consistent user counts
  If you're feeding the evaluation results into another system—like a visualization dashboard, a follow-up machine learning model, or a reporting tool—that expects the exact same number of users as your original test set, enabling preserve_rows=True keeps the row count intact. No missing users means no mismatches or errors in downstream processing.

As a quick contrast: the default preserve_rows=False is great when you only care about evaluating model performance on users who actually have positive interactions in the test set, as it focuses your analysis on the subset of users where the model has a chance to demonstrate meaningful performance.

内容的提问来源于stack exchange，提问作者RAbraham