movielens example: recall@k vs. precision@k vs. auc

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Hi

For the movielens example,

1) is it all right for Precision@K to have such a low score on test dataset i.e. 0.11 for test vs 0.6 on train set? Is this a case of it is better to have recommended than not?

2) is it better to normalize it with the maximum Precision@K that each user can attain for business evaluation and even for model comparison?

3) am i right to say that Precision@K for LightFM generally tends to suffer as compared to the Surprise library? This is because LightFM will recommend K number of recommendations regardless of actual rating - since LightFM uses relative scoring - whereas for the Surprise library, there is the notion of rating threshold and the latter might recommend less than K recommendations due to a minimum threshold requirement?

4) any reason why recall@k is not computed but LightFM has it implemented?

apologies for the many questions asked.

Start by reviewing the movielens example and its precision@k, recall@k, and AUC calculations, then consult the implemented recall@k metric. Done requires a maintainer-supported clarification of the four metric questions and, if a change is intended, a specified scope; the issue names no files or tests.

Written by the indexing model from the issue text.