 # Neural Collaborative Filtering for Personalized Ranking

this code all_items = set([i for i in range(**num_users**)]) should be
all_items = set([i for i in range(**num_items**)])

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For each user, the items that a user has not interacted with are candidate items (unobserved entries). The following function takes users identity and candidate items as input, and samples negative items randomly for each user from the candidate set of that user.

Here, in the context, the words “candidate items” indicate unobserved items for a user. However, this is contradictory with the code, where the variable “candidates” indicate the rated items for a user.

auc = 1.0 * (max - hits_all) / max if len(hits_all) > 0 else 0

How does this formula correspond to the auc definition metioned above in the context?

Hi @Shishir, you are right, this is a typo. Thanks for pointing out.

@peng, yeah. I should use another name instead of candidates for it.

Maybe you can check the way the authoer loads the train_iter/test_iter, which is in the mode ‘seq_ware’ . So actually when we do loop in users, each time we query the user’s most recent behaviour in the test_iter(a key-val dictionary) and then the result list(which length is one and conatins the item_id) is passed to the evaluate function. Moreover, in the hit_and_auc function we try to find whether the item the net “recommend” is in the top-k list or what’s the rank of that “recommendation” in the hits_al list(which is sorted by the score given by the net)，which is corresponding to the way we calculate the AUC (find out how many false recommendations rank before the ground truth)

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Why is the hadamard product used in the GMF?

From what I recall, a Matrix Factorization is defined as multipliying two matrices, usually of lower rank as the target matrix, using usual matrix multiplication, i. e. trying to reconstruct X as X’ = A \dot B with some reconstruction error. Is there some additional information available on this?

In the original paper, they wanna add some nonlinear layers over the dot product so they used element-wise multiplication in order to get a vector.

X’ = A \dot B is seen from the whole matrix level. If we look at it from each recommendation level, we need a score for each user-item pair. So that is vector-vector multiplication instead of matrix multiplication. Hope this helps.

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Thank you, this is a good answer.

Is it correct that there is a sigmoid activation in the last layer given that the BPR loss then applies a sigmoid again?