Click-Through Rate (CTR) prediction has become an essential task in digital
industries, such as digital advertising or online shopping. Many deep
learning-based methods have been implemented and have become state-of-the-art
models in the domain. To further improve the performance of CTR models,
Knowledge Distillation based approaches have been widely used. However, most of
the current CTR prediction models do not have much complex architectures, so
it's hard to call one of them 'cumbersome' and the other one 'tiny'. On the
other hand, the performance gap is also not very large between complex and
simple models. So, distilling knowledge from one model to the other could not
be worth the effort. Under these considerations, Mutual Learning could be a
better approach, since all the models could be improved mutually. In this
paper, we showed how useful the mutual learning algorithm could be when it is
between equals. In our experiments on the Criteo and Avazu datasets, the mutual
learning algorithm improved the performance of the model by up to 0.66%
relative improvement.

通过模型之间的相互学习算法，提高了点击率（CTR）预测模型在 Criteo 和 Avazu 数据集上的性能，相对提升了 0.66%。

细化点击率预测模型的相互学习

Mutual Learning for Finetuning Click-Through Rate Prediction Models

In e-commerce platforms, the relevant recommendation is a unique scenario
providing related items for a trigger item that users are interested in.
However, users' preferences for the similarity and diversity of recommendation
results are dynamic and vary under different conditions. Moreover, individual
item-level diversity is too coarse-grained since all recommended items are
related to the trigger item. Thus, the two main challenges are to learn
fine-grained representations of similarity and diversity and capture users'
dynamic preferences for them under different conditions. To address these
challenges, we propose a novel method called the Dynamic Preference-based and
Attribute-aware Network (DPAN) for predicting Click-Through Rate (CTR) in
relevant recommendations. Specifically, based on Attribute-aware Activation
Values Generation (AAVG), Bi-dimensional Compression-based Re-expression (BCR)
is designed to obtain similarity and diversity representations of user
interests and item information. Then Shallow and Deep Union-based Fusion (SDUF)
is proposed to capture users' dynamic preferences for the diverse degree of
recommendation results according to various conditions. DPAN has demonstrated
its effectiveness through extensive offline experiments and online A/B testing,
resulting in a significant 7.62% improvement in CTR. Currently, DPAN has been
successfully deployed on our e-commerce platform serving the primary traffic
for relevant recommendations. The code of DPAN has been made publicly
available.

电子商务平台中，我们提出了一种名为 DPAN 的新方法，通过学习细粒度的相似性和多样性表示并捕捉用户在不同条件下的动态偏好，来预测相关推荐中的点击率（CTR），经过离线实验和在线 A/B 测试的验证，CTR 提高了 7.62%。

基于偏好动态调整和属性感知的相关推荐网络：DPAN

DPAN: Dynamic Preference-based and Attribute-aware Network for Relevant  Recommendations

In many classical e-commerce platforms, personalized recommendation has been
proven to be of great business value, which can improve user satisfaction and
increase the revenue of platforms. In this paper, we present a new
recommendation problem, Trigger-Induced Recommendation (TIR), where users'
instant interest can be explicitly induced with a trigger item and follow-up
related target items are recommended accordingly. TIR has become ubiquitous and
popular in e-commerce platforms. In this paper, we figure out that although
existing recommendation models are effective in traditional recommendation
scenarios by mining users' interests based on their massive historical
behaviors, they are struggling in discovering users' instant interests in the
TIR scenario due to the discrepancy between these scenarios, resulting in
inferior performance. To tackle the problem, we propose a novel recommendation
method named Deep Interest Highlight Network (DIHN) for Click-Through Rate
(CTR) prediction in TIR scenarios. It has three main components including 1)
User Intent Network (UIN), which responds to generate a precise probability
score to predict user's intent on the trigger item; 2) Fusion Embedding Module
(FEM), which adaptively fuses trigger item and target item embeddings based on
the prediction from UIN; and (3) Hybrid Interest Extracting Module (HIEM),
which can effectively highlight users' instant interest from their behaviors
based on the result of FEM. Extensive offline and online evaluations on a
real-world e-commerce platform demonstrate the superiority of DIHN over
state-of-the-art methods.

本文提出一种新的推荐问题，即可以明确引起用户瞬时兴趣的触发项，并根据其推荐相关的库存项。通过在真实的电子商务平台上的离线和在线评估，本研究证明了 Deep Interest Highlight Network (DIHN) 在 Trigger-Induced Recommendation (TIR) 场景下胜过现有的方法。