DIN模型没用上user特征？

[zuowang]

@zhougr1993 在下面代码中，u_emb 没用的啊？ u_emb = hist 这里吧u_emb覆盖了，大佬能帮忙解释一下吗？

class Model(object):

  def __init__(self, user_count, item_count, cate_count, cate_list):

    self.u = tf.placeholder(tf.int32, [None,]) # [B]
    self.i = tf.placeholder(tf.int32, [None,]) # [B]
    self.j = tf.placeholder(tf.int32, [None,]) # [B]
    self.y = tf.placeholder(tf.float32, [None,]) # [B]
    self.hist_i = tf.placeholder(tf.int32, [None, None]) # [B, T]
    self.sl = tf.placeholder(tf.int32, [None,]) # [B]
    self.lr = tf.placeholder(tf.float64, [])

    hidden_units = 128

    user_emb_w = tf.get_variable("user_emb_w", [user_count, hidden_units])
    item_emb_w = tf.get_variable("item_emb_w", [item_count, hidden_units // 2])
    item_b = tf.get_variable("item_b", [item_count],
                             initializer=tf.constant_initializer(0.0))
    cate_emb_w = tf.get_variable("cate_emb_w", [cate_count, hidden_units // 2])
    cate_list = tf.convert_to_tensor(cate_list, dtype=tf.int64)

    u_emb = tf.nn.embedding_lookup(user_emb_w, self.u)

    ic = tf.gather(cate_list, self.i)
    i_emb = tf.concat(values = [
        tf.nn.embedding_lookup(item_emb_w, self.i),
        tf.nn.embedding_lookup(cate_emb_w, ic),
        ], axis=1)
    i_b = tf.gather(item_b, self.i)

    jc = tf.gather(cate_list, self.j)
    j_emb = tf.concat([
        tf.nn.embedding_lookup(item_emb_w, self.j),
        tf.nn.embedding_lookup(cate_emb_w, jc),
        ], axis=1)
    j_b = tf.gather(item_b, self.j)

    hc = tf.gather(cate_list, self.hist_i)
    h_emb = tf.concat([
        tf.nn.embedding_lookup(item_emb_w, self.hist_i),
        tf.nn.embedding_lookup(cate_emb_w, hc),
        ], axis=2)

    hist =attention(i_emb, h_emb, self.sl)
    #-- attention end ---

    hist = tf.layers.batch_normalization(inputs = hist)
    hist = tf.reshape(hist, [-1, hidden_units])
    hist = tf.layers.dense(hist, hidden_units)

    u_emb = hist
    print u_emb.get_shape().as_list()
    print i_emb.get_shape().as_list()
    print j_emb.get_shape().as_list()
    #-- fcn begin -------
    din_i = tf.concat([u_emb, i_emb], axis=-1)
    din_i = tf.layers.batch_normalization(inputs=din_i, name='b1')
    d_layer_1_i = tf.layers.dense(din_i, 80, activation=tf.nn.sigmoid, name='f1')
    #if u want try dice change sigmoid to None and add dice layer like following two lines. You can also find model_dice.py in this folder.
    #d_layer_1_i = tf.layers.dense(din_i, 80, activation=None, name='f1')
    #d_layer_1_i = dice(d_layer_1_i, name='dice_1_i')
    d_layer_2_i = tf.layers.dense(d_layer_1_i, 40, activation=tf.nn.sigmoid, name='f2')
    #d_layer_2_i = dice(d_layer_2_i, name='dice_2_i')
    d_layer_3_i = tf.layers.dense(d_layer_2_i, 1, activation=None, name='f3')
    din_j = tf.concat([u_emb, j_emb], axis=-1)
    din_j = tf.layers.batch_normalization(inputs=din_j, name='b1', reuse=True)
    d_layer_1_j = tf.layers.dense(din_j, 80, activation=tf.nn.sigmoid, name='f1', reuse=True)
    #d_layer_1_j = dice(d_layer_1_j, name='dice_1_j')
    d_layer_2_j = tf.layers.dense(d_layer_1_j, 40, activation=tf.nn.sigmoid, name='f2', reuse=True)
    #d_layer_2_j = dice(d_layer_2_j, name='dice_2_j')
    d_layer_3_j = tf.layers.dense(d_layer_2_j, 1, activation=None, name='f3', reuse=True)
    d_layer_3_i = tf.reshape(d_layer_3_i, [-1])
    d_layer_3_j = tf.reshape(d_layer_3_j, [-1])
    x = i_b - j_b + d_layer_3_i - d_layer_3_j # [B]
    self.logits = i_b + d_layer_3_i
    u_emb_all = tf.expand_dims(u_emb, 1)
    u_emb_all = tf.tile(u_emb_all, [1, item_count, 1])
    # logits for all item:
    all_emb = tf.concat([
        item_emb_w,
        tf.nn.embedding_lookup(cate_emb_w, cate_list)
        ], axis=1)
    all_emb = tf.expand_dims(all_emb, 0)
    all_emb = tf.tile(all_emb, [512, 1, 1])
    din_all = tf.concat([u_emb_all, all_emb], axis=-1)
    din_all = tf.layers.batch_normalization(inputs=din_all, name='b1', reuse=True)
    d_layer_1_all = tf.layers.dense(din_all, 80, activation=tf.nn.sigmoid, name='f1', reuse=True)
    #d_layer_1_all = dice(d_layer_1_all, name='dice_1_all')
    d_layer_2_all = tf.layers.dense(d_layer_1_all, 40, activation=tf.nn.sigmoid, name='f2', reuse=True)
    #d_layer_2_all = dice(d_layer_2_all, name='dice_2_all')
    d_layer_3_all = tf.layers.dense(d_layer_2_all, 1, activation=None, name='f3', reuse=True)
    d_layer_3_all = tf.reshape(d_layer_3_all, [-1, item_count])
    self.logits_all = tf.sigmoid(item_b + d_layer_3_all)
    #-- fcn end -------

    self.mf_auc = tf.reduce_mean(tf.to_float(x > 0))
    self.score_i = tf.sigmoid(i_b + d_layer_3_i)
    self.score_j = tf.sigmoid(j_b + d_layer_3_j)
    self.score_i = tf.reshape(self.score_i, [-1, 1])
    self.score_j = tf.reshape(self.score_j, [-1, 1])
    self.p_and_n = tf.concat([self.score_i, self.score_j], axis=-1)
    print self.p_and_n.get_shape().as_list()

    # Step variable
    self.global_step = tf.Variable(0, trainable=False, name='global_step')
    self.global_epoch_step = \
        tf.Variable(0, trainable=False, name='global_epoch_step')
    self.global_epoch_step_op = \
        tf.assign(self.global_epoch_step, self.global_epoch_step+1)

    self.loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=self.logits,
            labels=self.y)
        )

    trainable_params = tf.trainable_variables()
    self.opt = tf.train.GradientDescentOptimizer(learning_rate=self.lr)
    gradients = tf.gradients(self.loss, trainable_params)
    clip_gradients, _ = tf.clip_by_global_norm(gradients, 5)
    self.train_op = self.opt.apply_gradients(
        zip(clip_gradients, trainable_params), global_step=self.global_step)

[zhougr1993]

别方 hist就是用户的历史行为list的embedding 序列 这个就是用户的特征 u_emb只是对user id的embedding而已 确实没用 不影响实验

[zuowang]

多谢