Modeling user's historical feedback is essential for Click-Through Rate
Prediction in personalized search and recommendation. Existing methods usually
only model users' positive feedback information such as click sequences which
neglects the context information of the feedback. In this paper, we propose a
new perspective for context-aware users' behavior modeling by including the
whole page-wisely exposed products and the corresponding feedback as
contextualized page-wise feedback sequence. The intra-page context information
and inter-page interest evolution can be captured to learn more specific user
preference. We design a novel neural ranking model RACP(i.e., Recurrent
Attention over Contextualized Page sequence), which utilizes page-context aware
attention to model the intra-page context. A recurrent attention process is
used to model the cross-page interest convergence evolution as denoising the
interest in the previous pages. Experiments on public and real-world industrial
datasets verify our model's effectiveness.

该论文提出了一种新的上下文感知用户行为建模思路，通过包含完整页面上暴露的产品和相应的反馈作为上下文的页面级反馈序列，可以捕捉到页面内上下文信息和页面间兴趣演化，以学习更具体的用户偏好。作者设计了一种采用页面上下文感知注意力的新型神经排序模型 RACP，使用递归注意过程来建模页面间的兴趣收敛演化，实验在公开数据集和真实工业数据集上验证了该模型的有效性。

建模电商搜索中用户的上下文化页面反馈，用以预测点击率

Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search

Search in social networks such as Facebook poses different challenges than in
classical web search: besides the query text, it is important to take into
account the searcher's context to provide relevant results. Their social graph
is an integral part of this context and is a unique aspect of Facebook search.
While embedding-based retrieval (EBR) has been applied in eb search engines for
years, Facebook search was still mainly based on a Boolean matching model. In
this paper, we discuss the techniques for applying EBR to a Facebook Search
system. We introduce the unified embedding framework developed to model
semantic embeddings for personalized search, and the system to serve
embedding-based retrieval in a typical search system based on an inverted
index. We discuss various tricks and experiences on end-to-end optimization of
the whole system, including ANN parameter tuning and full-stack optimization.
Finally, we present our progress on two selected advanced topics about
modeling. We evaluated EBR on verticals for Facebook Search with significant
metrics gains observed in online A/B experiments. We believe this paper will
provide useful insights and experiences to help people on developing
embedding-based retrieval systems in search engines.

本研究探讨如何将基于嵌入式检索的技术应用于 Facebook 搜索系统，介绍了用于建模语义嵌入的统一嵌入框架和在基于倒排索引的典型搜索系统中提供嵌入式检索的系统，以及整个系统的端到端优化技巧和经验。该方法可显著改善 Facebook 搜索垂直领域的度量标准。

Facebook 搜索中基于嵌入的检索

Embedding-based Retrieval in Facebook Search

An increasing number of machine learning tasks require dealing with large
graph datasets, which capture rich and complex relationship among potentially
billions of elements. Graph Neural Network (GNN) becomes an effective way to
address the graph learning problem by converting the graph data into a low
dimensional space while keeping both the structural and property information to
the maximum extent and constructing a neural network for training and
referencing. However, it is challenging to provide an efficient graph storage
and computation capabilities to facilitate GNN training and enable development
of new GNN algorithms. In this paper, we present a comprehensive graph neural
network system, namely AliGraph, which consists of distributed graph storage,
optimized sampling operators and runtime to efficiently support not only
existing popular GNNs but also a series of in-house developed ones for
different scenarios. The system is currently deployed at Alibaba to support a
variety of business scenarios, including product recommendation and
personalized search at Alibaba's E-Commerce platform. By conducting extensive
experiments on a real-world dataset with 492.90 million vertices, 6.82 billion
edges and rich attributes, AliGraph performs an order of magnitude faster in
terms of graph building (5 minutes vs hours reported from the state-of-the-art
PowerGraph platform). At training, AliGraph runs 40%-50% faster with the novel
caching strategy and demonstrates around 12 times speed up with the improved
runtime. In addition, our in-house developed GNN models all showcase their
statistically significant superiorities in terms of both effectiveness and
efficiency (e.g., 4.12%-17.19% lift by F1 scores).

通过构建一个分布式图存储的 AliGraph 系统，本论文针对大型图数据集上的机器学习问题，实现了高效的图神经网络训练和开发，并在阿里巴巴的电商平台上实际应用于产品推荐和个性化搜索等业务场景中，提高了训练效率和模型效果。