Click-Through Rate (CTR) prediction is one of the core tasks in recommender
systems (RS). It predicts a personalized click probability for each user-item
pair. Recently, researchers have found that the performance of CTR model can be
improved greatly by taking user behavior sequence into consideration,
especially long-term user behavior sequence. The report on an e-commerce
website shows that 23\% of users have more than 1000 clicks during the past 5
months. Though there are numerous works focus on modeling sequential user
behaviors, few works can handle long-term user behavior sequence due to the
strict inference time constraint in real world system. Two-stage methods are
proposed to push the limit for better performance. At the first stage, an
auxiliary task is designed to retrieve the top-$k$ similar items from long-term
user behavior sequence. At the second stage, the classical attention mechanism
is conducted between the candidate item and $k$ items selected in the first
stage. However, information gap happens between retrieval stage and the main
CTR task. This goal divergence can greatly diminishing the performance gain of
long-term user sequence. In this paper, inspired by Reformer, we propose a
locality-sensitive hashing (LSH) method called ETA (End-to-end Target
Attention) which can greatly reduce the training and inference cost and make
the end-to-end training with long-term user behavior sequence possible. Both
offline and online experiments confirm the effectiveness of our model. We
deploy ETA into a large-scale real world E-commerce system and achieve extra
3.1\% improvements on GMV (Gross Merchandise Value) compared to a two-stage
long user sequence CTR model.

该研究提出了一种叫做 ETA 的局部敏感哈希方法，可以大大降低训练和推理成本，并使得可以使用长期用户行为序列进行端到端训练，以实现更好的点击率预测表现。

点击率预测模型中的用户行为端到端检索

End-to-End User Behavior Retrieval in Click-Through RatePrediction Model

With the explosive growth of e-commerce, online transaction fraud has become
one of the biggest challenges for e-commerce platforms. The historical
behaviors of users provide rich information for digging into the users' fraud
risk. While considerable efforts have been made in this direction, a
long-standing challenge is how to effectively exploit internal user information
and provide explainable prediction results. In fact, the value variations of
same field from different events and the interactions of different fields
inside one event have proven to be strong indicators for fraudulent behaviors.
In this paper, we propose the Dual Importance-aware Factorization Machines
(DIFM), which exploits the internal field information among users' behavior
sequence from dual perspectives, i.e., field value variations and field
interactions simultaneously for fraud detection. The proposed model is deployed
in the risk management system of one of the world's largest e-commerce
platforms, which utilize it to provide real-time transaction fraud detection.
Experimental results on real industrial data from different regions in the
platform clearly demonstrate that our model achieves significant improvements
compared with various state-of-the-art baseline models. Moreover, the DIFM
could also give an insight into the explanation of the prediction results from
dual perspectives.

本文提出利用用户行为序列中的内部字段信息，从值变化和字段交互的双重视角利用 Dual Importance-aware Factorization Machines （DIFM）在实时交易欺诈检测方面实现显著的改进。