Neural Collaborative Filtering for Personalized Ranking

Good example:)
Can we define a gluon.dataset like below for putting negative sampling inside train iter instead of calling this function per iteration? I think this may be helpful for simplifying the training progress and reducing memory usage especially dealing with larger dataset like movielens 20 million and other.

def __getitem__(self, idx):
    if idx % (self.nb_neg + 1) == 0:
        idx = idx // (self.nb_neg + 1)
        return[idx][0],[idx][1], np.ones(1, dtype=np.float32).item()
        idx = idx // (self.nb_neg + 1)
        u =[idx][0]
        j = mx.random.randint(0, self.nb_items).asnumpy().item()
        while (u, j) in self.mat:
            j = mx.random.randint(0, self.nb_items).asnumpy().item()
        return u, j, np.zeros(1, dtype=np.float32).item()
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Thank you very much for providing the code, I will give it a test ! If it helps, we will revise it.:smiley:

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thank you so much. i will try

Hi, I think the ‘num_users’ in evaluate_ranking function should be ‘num_items’.

def evaluate_ranking(net, test_input, seq, candidates, num_users, num_items,
    ranked_list, ranked_items, hit_rate, auc = {}, {}, [], []
    # all_items = set([i for i in range(num_users)])
    all_items = set([i for i in range(num_items)])

You have a typo - eastimating should be estimating. Your typo did however make me hungry :slight_smile: