dygrec/asrep

Released code of SIGIR2021 Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer.

asrep's Issues

a doubt in DataProcessing.py

user_train_reverse = {}
user_valid_reverse = {}
user_test_reverse = {}
for user in User_forreversed:
    nfeedback = len(User_forreversed[user])
    if nfeedback < 3:
        user_train_reverse[user] = User_forreversed[user]
        user_valid_reverse[user] = []
        user_test_reverse[user] = []
    else:
        user_train_reverse[user] = User[user][:-2]
        user_valid_reverse[user] = []
        user_valid_reverse[user].append(User[user][-2])
        user_test_reverse[user] = []
        user_test_reverse[user].append(User[user][-1])

I download and run your code,found the train_reverse.txt same as train.txt .
In this circle ,after else ,the list should use User_forreversed?
Looking forward to your reply.

A doubt about data augmentation

Thanks for your nice work, but the detail of data augmentation may have a leakage problem. More precisely, the pseudo-prior items may see the test information ahead of the inference.

def data_augment(model, dataset, args, sess, gen_num):

    [train, valid, test, original_train, usernum, itemnum] = copy.deepcopy(dataset)
    all_users = list(train.keys())

    cumulative_preds = defaultdict(list)
    for num_ind in range(gen_num):
        batch_seq = []
        batch_u = []
        batch_item_idx = []

        for u_ind, u in enumerate(all_users):
            u_data = train.get(u, []) + valid.get(u, []) + test.get(u, []) + cumulative_preds.get(u, [])

            if len(u_data) == 0 or len(u_data) >= args.M: continue

            seq = np.zeros([args.maxlen], dtype=np.int32)
            idx = args.maxlen - 1
            for i in reversed(u_data):
                if idx == -1: break
                seq[idx] = i
                idx -= 1
            rated = set(u_data)
            item_idx = list(set([i for i in range(itemnum)]) - rated) 

            batch_seq.append(seq)
            batch_item_idx.append(item_idx)
            batch_u.append(u)

The user data (i.e. ‘u_data = train.get(u, []) + valid.get(u, []) + test.get(u, []) + cumulative_preds.get(u, [])’) consist of the test data and used for generate the prior data. And the augmented data (i.e. prior data + train data + valid data) training the left-to-right model in the fine-tuning stage and the model to infer the rec result. So both augmented data and the left-to-right model see the test data(leakage of the test data) ahead of the inference.

problems with item order in a sequence

When I try to pretrain your model to yield pseudo items, it seems that in your code, the actual item order in a sequence is inconsistent with the proposed model in your paper.
For example, assume you have a sequence with items [1,2,3,4,5,6]. When you try to reverse-train the model, you should input [6,5,4,3,2] as training user sequence and [5,4,3,2,1] as positive user sequence. But when I try to examine this, I get very confusing sequence like [5,4,2,1,3].

def sample_function(user_train, usernum, itemnum, batch_size, maxlen, result_queue, SEED):
    def sample():
        user = np.random.randint(1, usernum + 1)
        while len(user_train[user]) <= 1: user = np.random.randint(1, usernum + 1)

        seq = np.zeros([maxlen], dtype=np.int32)
        pos = np.zeros([maxlen], dtype=np.int32)
        neg = np.zeros([maxlen], dtype=np.int32)
        nxt = user_train[user][-1]
        idx = maxlen - 1

        ts = set(user_train[user])
        for i in reversed(user_train[user][:-1]):
            seq[idx] = i
            pos[idx] = nxt
            if nxt != 0: neg[idx] = random_neq(1, itemnum + 1, ts)
            nxt = i
            idx -= 1
            if idx == -1: break

        return (user, seq, pos, neg)

    np.random.seed(SEED)
    while True:
        one_batch = []
        for i in range(batch_size):
            one_batch.append(sample())
            print(one_batch[-1]) #something went wrong here
        result_queue.put(zip(*one_batch))

What's more, the item order is also suspicious when I try to reverse-predict pseudo items based on trained model.
According to this code, train, test, valid, and cumulatively-predicted items are concatenated orderly and then fed into transformer model to yield next pseudo item.
u_data = train.get(u, []) + valid.get(u, []) + test.get(u, []) + cumulative_preds.get(u, [])
But if you want to predict pseudo items, shouldn't you put cumulative predictions at the beginning of the original sequence so the order should be u_data = cumulative_preds.get(u, [])+train.get(u, []) + valid.get(u, []) + test.get(u, []) ?
Looking forward to your reply
Thanks