hahmad2008

@massquantity Thank you I will check and get back to you. I am just asking because when I run split function from both librecommender and the other repo. it took...

@massquantity I have the following notes: 1- There is a parameter for negative samples in `NCF`: `num_neg`, so this parameter used for generating negative samples for training, then we don't...

@massquantity Thank you for your answer. For NCF in Recommender, I can't initiate NCF instance[ `data = NCFDataset(train=train, test=test, seed=SEED)`](https://github.com/microsoft/recommenders/blob/main/reco_utils/recommender/ncf/dataset.py), it is not only slow but it is also [memory...

@massquantity For loss, we can also evaluate the recommendation using MAP as we do in ranking. so we don't only predict the same seen items for the user with different...

@massquantity sorry I meant 1.6. million records from data-frame, including: * Number of users: 82k * Number of items: 32k So it can't initiate the [NCF dataset ](https://github.com/microsoft/recommenders/blob/main/reco_utils/recommender/ncf/dataset.py)from this dataframe....

@massquantity Thank you for this explanation. But is reindexing user/item and generate negative samples needs to generate the whole n x m matrix! Weird! this reminds me of traditional memory-based...

@massquantity I think the difference in evaluation metric used, for Recommender repo consider the problem as ranking so it used MAP as a loss. but for LibRecommender used MSE as...

Thank you @massquantity for your time. I will check that

@massquantity How can I check the `split_by_ratio_chrono` after splitting? for example how to get items and labels for a specific user. from the original data we can take it like...

@massquantity If I convert the users/ items ids as following, can I guarantee that model.predict(user=id) id in data is the same as id for mode prediction and recommendation? ``` items...