User historical behaviors are proved useful for Click Through Rate (CTR)
prediction in online advertising system. In Meituan, one of the largest
e-commerce platform in China, an item is typically displayed with its image and
whether a user clicks the item or not is usually influenced by its image, which
implies that user's image behaviors are helpful for understanding user's visual
preference and improving the accuracy of CTR prediction. Existing user image
behavior models typically use a two-stage architecture, which extracts visual
embeddings of images through off-the-shelf Convolutional Neural Networks (CNNs)
in the first stage, and then jointly trains a CTR model with those visual
embeddings and non-visual features. We find that the two-stage architecture is
sub-optimal for CTR prediction. Meanwhile, precisely labeled categories in
online ad systems contain abundant visual prior information, which can enhance
the modeling of user image behaviors. However, off-the-shelf CNNs without
category prior may extract category unrelated features, limiting CNN's
expression ability. To address the two issues, we propose a hybrid CNN based
attention module, unifying user's image behaviors and category prior, for CTR
prediction. Our approach achieves significant improvements in both online and
offline experiments on a billion scale real serving dataset.

本文介绍了如何通过混合 CNN 技术和类别先验信息，在 CTR 预测中提高广告点击率，取得了显著的后验实验结果和在线实验结果。

基于混合卷积神经网络和类别先验的用户图像行为建模注意力机制

Hybrid CNN Based Attention with Category Prior for User Image Behavior Modeling

Advertising and feed ranking are essential to many Internet companies such as
Facebook. Among many real-world advertising and feed ranking systems, click
through rate (CTR) prediction plays a central role. In recent years, many
neural network based CTR models have been proposed and achieved success such as
Factorization-Machine Supported Neural Networks, DeepFM and xDeepFM. Many of
them contain two commonly used components: embedding layer and MLP hidden
layers. On the other side, gating mechanism is also widely applied in many
research fields such as computer vision(CV) and natural language
processing(NLP). Some research has proved that gating mechanism improves the
trainability of non-convex deep neural networks. Inspired by these
observations, we propose a novel model named GateNet which introduces either
the feature embedding gate or the hidden gate to the embedding layer or hidden
layers of DNN CTR models, respectively. The feature embedding gate provides a
learnable feature gating module to select salient latent information from the
feature-level. The hidden gate helps the model to implicitly capture the
high-order interaction more effectively. Extensive experiments conducted on
three real-world datasets demonstrate its effectiveness to boost the
performance of various state-of-the-art models such as FM, DeepFM and xDeepFM
on all datasets.

GateNet 模型提出了基于门控机制的神经网络 CTR 模型，引入特征嵌入门或隐藏门来提升模型性能，该模型在真实数据集上得到了有效验证。

GateNet: 增强门控深度网络用于点击率预测

GateNet: Gating-Enhanced Deep Network for Click-Through Rate Prediction

Advertising and feed ranking are essential to many Internet companies such as
Facebook and Sina Weibo. Among many real-world advertising and feed ranking
systems, click through rate (CTR) prediction plays a central role. There are
many proposed models in this field such as logistic regression, tree based
models, factorization machine based models and deep learning based CTR models.
However, many current works calculate the feature interactions in a simple way
such as Hadamard product and inner product and they care less about the
importance of features. In this paper, a new model named FiBiNET as an
abbreviation for Feature Importance and Bilinear feature Interaction NETwork is
proposed to dynamically learn the feature importance and fine-grained feature
interactions. On the one hand, the FiBiNET can dynamically learn the importance
of features via the Squeeze-Excitation network (SENET) mechanism; on the other
hand, it is able to effectively learn the feature interactions via bilinear
function. We conduct extensive experiments on two real-world datasets and show
that our shallow model outperforms other shallow models such as factorization
machine(FM) and field-aware factorization machine(FFM). In order to improve
performance further, we combine a classical deep neural network(DNN) component
with the shallow model to be a deep model. The deep FiBiNET consistently
outperforms the other state-of-the-art deep models such as DeepFM and extreme
deep factorization machine(XdeepFM).

本文提出了 FiBiNET 模型，通过 Squeeze-Excitation 网络机制学习特征重要性，通过双线性函数有效地学习特征交互，结果显示该模型在真实数据集上的性能优于其他浅层模型和深度模型。

FiBiNET：结合特征重要性和双线性特征交互预测点击率

FiBiNET: Combining Feature Importance and Bilinear feature Interaction  for Click-Through Rate Prediction

Nowadays, more and more customers browse and purchase products in favor of
using mobile E-Commerce Apps such as Taobao and Amazon. Since merchants are
usually inclined to describe redundant and over-informative product titles to
attract attentions from customers, it is important to concisely display short
product titles on limited screen of mobile phones. To address this discrepancy,
previous studies mainly consider textual information of long product titles and
lacks of human-like view during training and evaluation process. In this paper,
we propose a Multi-Modal Generative Adversarial Network (MM-GAN) for short
product title generation in E-Commerce, which innovatively incorporates image
information and attribute tags from product, as well as textual information
from original long titles. MM-GAN poses short title generation as a
reinforcement learning process, where the generated titles are evaluated by the
discriminator in a human-like view. Extensive experiments on a large-scale
E-Commerce dataset demonstrate that our algorithm outperforms other
state-of-the-art methods. Moreover, we deploy our model into a real-world
online E-Commerce environment and effectively boost the performance of click
through rate and click conversion rate by 1.66% and 1.87%, respectively.

本文提出了一种用于移动电商下的产品标题生成算法 MM-GAN，该算法从原始长标题中提取文本信息、产品的图像信息和属性标签等多模态信息，并将短标题生成视为一个强化学习过程。在进行模型训练和评估时，以类人的视角评估生成的标题，该算法在大规模电商数据集上的实验证明，与其他方法相比，本算法表现更优，应用于真实世界的电商平台后，点击率和点击转化率均有明显提高。