Which metrics for analysis and evaluation for implicit data in a recommender system do you use? And which ones do you use when you are looking for the closest neighbors to make a recommendation?

I'm using the NCF model.

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The architecture of a Neural Collaborative Filtering model. Taken from the Neural Collaborative Filtering paper.

First I let the model train with the help of the NCF model. Then I find the closest neighbors with k-means.

I found metrics like MSE, RMSE, Precision, Recall, ... and What metric should I use for assessing Implicit matrix factorization recommender with ALS? . I'm not sure which ones are best and how I can then determine whether the closest neighbors are good or bad.

  • What metrics are there to evaluate the model?
  • What metrics are there to evaluate whether the neighbors found are "good"?


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