Text Regression for Click-through Rate Prediction Using Convnet
These last years, deep learning has been successfully applied to Natural Language Processing.
By their ability to model sequential phenoma, Recurrent Neural Networks-based models have obtained state of the art results on difficult tasks including speech recognition [5], image captioning [6] and machine translation [7].
In less structured tasks like text classification, character-level CNN architectures have also shown promising results.
Zhang et al. [1] achieved state-of-the arts results on several classification datasets like Yelp Review Polarity, Yahoo! Answers or Amazon Review. More recently, Alexis Conneau et al. [2] improved these results with a deeper char-level CNN using shortcut connections [3] and temporal Batch Normalization [4].
In this post we will present how this kind of architecture can be applied to click-through rate prediction using TenforFlow and using only a few training samples. In particular, we are interested in predicting the click-through rate of Outbrain article recommendations, given only their title (in french language).
The code to reproduce this post’s results can be found here: https://github.com/Deepomatic/regression-char-level-cnn-tf
DATA
We have collected 46,458 pairs of article title / CTR from production data.
The dataset was then reduced to 2,886 pairs by removing all recommendations displayed less than 100 times: 2,634for training and 254 for validation.
Each title correspond to an Outbrain’s recommendation, as seen at the end of articles on various medias.
MODEL
Char-level networks process text as a succession of characters, without any knowledge of words, semantic or syntactic structures of any language.
Each character is quantized using a one-hot-vector representation of dimension 93, the size of our alphabet:
abcdefghijklmnopqrstuvwxyz0123456789-,;.!?:'\"/\\|_@#$%^&*~`+-=<>()[]{}\nêéèàôîïëçù€Titles are then represented by 93 * sequence_length embeddings, where sequence_length represents the title length.
import tensorflow as tfclass Network(object):
def __init__(self, alphabet_size):
self.inputs = tf.placeholder(tf.float32, [None, alphabet_size, None, 1], name="inputs")
self.labels = tf.placeholder(tf.float32, [None], name="labels")
self.dropout_keep_prob = tf.placeholder(tf.float32, name="dropout_keep_prob")
Our model is a 5-layers Convolution Neural Networks with: 2 max-pooling layers of kernel size 5 and 3 respectively,2 dropout layers of probability 0.4, and ends with a sigmoid.
It was then trained to minimize the squared error loss between groundtruth and predicted CTRs.
Unlike Zhang et al. [1] where the last layers are fully connected layers with fixed entry size, we opted for a fully convolutional approach. Our model can thus process batches of different dimensions between min_size andmax_size (here set to 96 and 156 repectively) characters by padding each title with (0,0,…,0) vectors to reach the maximum title length within the batch. When the title length was greater than max_size, we randomly choose a subset of it.
By using this technique, we were able to train our model more efficiently and reached a better minimum than with fixed entry size models.
def deploy(self):
conv0 = self.convolution(input=self.inputs, nOut=128, kH=self.inputs.get_shape().dims[1].value, kW=5, strides=[1, 1, 1, 1])
pool0 = self.pooling(input=conv0, ksize=[1, 1, 2, 1], strides=[1, 1, 2, 1], pooling='max') conv1 = self.convolution(input=pool0, nOut=128, kH=1, kW=3, strides=[1, 1, 1, 1])
pool1 = self.pooling(input=conv1, ksize=[1, 1, 2, 1], strides=[1, 1, 2, 1], pooling='max')
pool1 = tf.nn.dropout(pool1, self.dropout_keep_prob, name='Dropout_{0}'.format(self.layer_key)) previous_shape = [dim.value or 22 for dim in pool1.get_shape().dims]
fc2 = self.convolution(input=pool1, nOut=256, kH=previous_shape[1], kW=previous_shape[2], strides=[1, 1, 1, 1])
fc2 = tf.nn.dropout(fc2, self.dropout_keep_prob, name='Dropout_{0}'.format(self.layer_key)) previous_shape = [dim.value or 1 for dim in fc2.get_shape().dims]
fc3 = self.convolution(input=fc2, nOut=256, kH=previous_shape[1], kW=previous_shape[2], strides=[1, 1, 1, 1]) previous_shape = [dim.value or 1 for dim in fc3.get_shape().dims]
fc4 = self.convolution(input=fc3, nOut=1, kH=previous_shape[1], kW=previous_shape[2], strides=[1, 1, 1, 1], non_linearity=None) fc4_hist_summary = tf.histogram_summary("fc4", fc4)
self.scores = tf.squeeze(tf.reduce_mean(tf.sigmoid(fc4), 2), name='scores')
We didn’t use any weight regularization as it has shown to degrade results. On the other hand, we added 2 dropout layers as shown in the graph figure to avoid too severe overfitting.
Finally, ADAM optimizer was used with a base learning rate equal to 1e-3 and beta1 was set to 0.5.
RESULTS
- squared error loss
- light blue = val, blue = train.
Because there are many titles with very low CTRs values, it is quite easy to obtained a low squared error loss.
In order to evaluate how our model understood the potential success of a title, we monitored two additional metrics:
- top-k retrieval value which measures the intersection of well ranked top-k CTR titles for k varying from 1 to 259
from scipy.integrate import quaddef topk(sorted_like_preds, sorted_like_groundtrouth, step, res_dir):
length = len(sorted_like_preds)
xs = range(length)
ys = []
print(sorted_like_preds)
for x in xs:
best_x = sorted_like_groundtrouth[-(x + 1):]
best_x_preds = sorted_like_preds[-(x + 1):]
dict_i = {}
dict_j = {}
for i, value_preds in enumerate(best_x_preds):
for j, value_gt in enumerate(best_x):
if value_preds == value_gt and not (i in dict_i or j in dict_j):
dict_i[i] = True
dict_j[j] = True nb_good = len(dict_i.keys())
acc_x = float(nb_good) / (x + 1)
ys.append(acc_x)
res = quad(lambda x:ys[int(x)], 0, len(ys) - 1, limit=10)[0] / length
Where a random guess will approximatively give an identity line with score 0.5. (It will not be exactly an identity line because, according to our scoring function, pairs of same CTR eases global ranking)
- rank2 value which measures the ratio of well-ranked pair-wise comparaisons between titles which have at least a CTR ratio of 200.
import sklearn.metrics as metricsdef rank2(y_preds, y_val, step, res_dir):
ratio = 2.
val_size = len(y_val)
scores = []
labels = []
for i in range(val_size-1):
for j in range(i + 1, val_size):
if max(y_val[i], y_val[j]) / max(1e-5, min(y_val[i], y_val[j])) > ratio:
labels.append((y_val[i] - y_val[j]) * (y_preds[i] - y_preds[j]) > 0)
score = np.abs(y_preds[i] - y_preds[j])
scores.append(score)
mAP = metrics.average_precision_score(labels, scores)
Where a random guess will give a constant function equal to 0.5.
EXAMPLES
In this section we present the 10 most and the 5 less successful articles according to our model as well as their groundtruth rank.
Highest predicted CTRs:
1) les anges 8 : andréane en dit plus sur son couple avec aurélie (groundtruth: 1st)
2) secret story, les 8 plus grosses prises de poids : jessica, nadège, aurélie… (groundtruth: 2nd)
3) nabilla et thomas vergara : révélations sur leur vie sexuelle (groundtruth: 13th)
4) la folle virée d’elodie frégé et joeystarr… kim kardashian, poupée pour sa fille… (groundtruth: 72th)
5) pamela anderson : entièrement nue, à 46 ans, pour une série photo érotique (groundtruth: 29th)
6) mort d’isabelle (secret story 2) : une femme généreuse qui avait peur de mourir (groundtruth: 3th)
7) patrick poivre d’arvor : claire chazal, la mort de ses trois filles et son fils françois (groundtruth: 4th)
8) 10 alcooliques qui ont marqué le monde par leur intelligence (groundtruth: 48th)
9) laurence chirac : sa vie hors de l’élysée et ses derniers jours dans l’ombre (groundtruth: 35th)
10) sylvie vartan présente sa fille, darina : “elle m’a apporté un coup de jeune” (groundtruth: 13th)
Lowest predicted CTRs:
250) mauvaise haleine ? ces 7 astuces simples vont y mettre un terme (groundtruth: 155th)
251) entre sculpture et photographie, huit artistes modernes au musée rodin (groundtruth: 230th)
252) les recettes de tartes aux artichauts (groundtruth: 223th)
253) pintadeau de la drôme sur canapé par alain ducasse (groundtruth: 242th)
254) recette de cuisses de grenouilles par alain ducasse: (groundtruth: 208th)
REFERENCES
[1] Xiang Zhang, Junbo Zhao and Yann Le Cun Character-level convolutional networks for text classification, 2015.
[2] Alexis Conneau, Holger Schwenk and Yann Le Cun Very Deep Convolutional Networks for Natural Language Processing, 2016.
[3] Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun Deep Residual Learning for Image Recognition, 2015.
[4] Sergey Ioffe and Christian Szegedy Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, 2015.
[5] Alex Graves, Abdel-rahman Mohamed and Geoffrey Hinton Speech Recognition with Deep Recurrent Neural Networks, 2013.
[6] Andrej Karpathy and Li Fei-Fei Deep Visual-Semantic Alignments for Generating Image Descriptions, 2014.
[7] Dzmitry Bahdanau, Kyunghyun Cho and Yoshua Bengio Neural Machine Translation by Jointly Learning to Align and Translate, 2014.
Thanks for reading this.
David Bouba, Deep learning scientist @Deepomatic
