longmaoteamtf/deep_recommenders

Deep Recommenders

deep_recommenders's Issues

google_tt模型中batch_softmax疑问

def corrected_batch_softmax(x, y, sampling_p=None):
    """logQ correction softmax"""
    correct_inner_product = log_q(x, y, sampling_p=sampling_p)
    return tf.math.exp(correct_inner_product) / tf.math.reduce_sum(tf.math.exp(correct_inner_product))

这里softmax计算的是不是有些问题呢?
原论文:

中分母是x_i与batch内不同y_j点乘的和, 而代码中分母是batch内x_i与y_i点乘的和

deep_retrieval 测试阶段报错NotImplementedError哎，楼主跑通了？

老哥，mmoe和esmm上线了吗，哪个效果好点

ModuleNotFoundError: No module named 'deep_recommenders'

大佬，请问这个错误，是需要pip安装deep_recommenders吗

Fix bug from `transformer.ipynb` in `/experiments`

Hi, thank you so much for open sourcing your project, it helps me a lot!!
Here I just have one simple bug to report:

---

Source: 3rd code cell from notebook

• Original:

outputs = Transformer(
    vocab_size, 
    model_dim, 
    n_heads=2, 
    encoder_stack=2,
    decoder_stack=2, 
    feed_forward_size=50
)([encoder_inputs, decoder_inputs])

After ran the notebook and traced your code (deep_recommenders/keras/models/nlp/Transformer.py),
the input of Transformer class object needs 2 parameters (encoder_inputs, decoder_inputs), it should not be a list.

• Revised:

outputs = Transformer(
    vocab_size, 
    model_dim, 
    n_heads=2, 
    encoder_stack=2,
    decoder_stack=2, 
    feed_forward_size=50
)(encoder_inputs, decoder_inputs)

DeepRecommend.src.transformer.train中的load_data的到的decoder_targets是全零arrays

源码：

 decoder_targets = np.zeros((len(answers_seqs), max_len, vocab_size), dtype='float32')
    for i, seq in enumerate(answers_seqs):
        for j, index in enumerate(seq):
            if j > 0: decoder_targets[i, j-1, index-1] = 1
            if index == 0: break

似乎是出问题在break处，因为padding为0，pad_sequences默认是 pre pad，所以遇见0 就break，导致decoder_targets未能成功赋值

这段挖掘hard 负样本的方法不太懂，还请大佬赐教

class HardNegativeMining(tf.keras.layers.Layer):
"""Hard Negative"""

def __init__(self, num_hard_negatives: int, **kwargs):
    super(HardNegativeMining, self).__init__(**kwargs)

    self._num_hard_negatives = num_hard_negatives

def call(self, logits: tf.Tensor, labels: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]:
    num_sampled = tf.minimum(self._num_hard_negatives + 1, tf.shape(logits)[1])

    _, indices = tf.nn.top_k(logits + labels * MAX_FLOAT, k=num_sampled, sorted=False)

    logits = _gather_elements_along_row(logits, indices)
    labels = _gather_elements_along_row(labels, indices)

    return logits, labels