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

[deckikwok]

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.

[Sachet12345]

Hi, did you get the answers to your questions?

[Chen-Cai-OSU]

Hi,

I am also interested in understandiny why matrix factorization performs poorly on Movielens data in terms of precison@k and recall@k. https://github.com/microsoft/recommenders/blob/main/examples/06_benchmarks/movielens.ipynb here listed some benchmark dataset where another MF method ALS is also doing poorly. Is this fair to conclude that most MF methods does not work well on moivelens?