Pytorch Version (BPR: Bayesian Personalized Ranking from Implicit Feedback) Algorithms
Note that I use the movie lens datasets (ml-1m) I used the code from https://github.com/guoyang9/BPR-pytorch
improment
- no negative sampe num in my case
- predict all negative samples for top k
"""" class BPRData(data.Dataset): def init(self, users, items, candidates, num_items, is_training = True): super(BPRData, self).init() self.users = users self.items = items self.is_training = is_training self.cand = candidates self.num_items = num_items self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def __len__(self):
return len(self.users)
def __getitem__(self, idx):
if self.is_training == True:
while True:
# Negative sample
positive_list = self.cand[int(self.users[idx])]
negative_sample = random.randint(0, self.num_items - 1)
if negative_sample not in positive_list:
break
user = self.users[idx]
item_i = self.items[idx]
item_j = negative_sample if \
self.is_training else self.items[idx]
return user, item_i, item_j
"""