Personalized recommendation system has become pervasive in various video
platform. Many effective methods have been proposed, but most of them didn't
capture the user's multi-level interest trait and dependencies between their
viewed micro-videos well. To solve these problems, we propose a Self-over-Co
Attention module to enhance user's interest representation. In particular, we
first use co-attention to model correlation patterns across different levels
and then use self-attention to model correlation patterns within a specific
level. Experimental results on filtered public datasets verify that our
presented module is useful.

提出了一种自我上下文关注模块来增强用户的兴趣表示，首先使用共同关注来对不同级别之间的相关模式进行建模，然后利用自我关注对特定级别内的相关模式进行建模。实验证明，该模块是有用的。

面向微视频推荐的多兴趣高阶交互建模

Modeling High-order Interactions across Multi-interests for Micro-video  Recommendation

The increasing interest in user privacy is leading to new privacy preserving
machine learning paradigms. In the Federated Learning paradigm, a master
machine learning model is distributed to user clients, the clients use their
locally stored data and model for both inference and calculating model updates.
The model updates are sent back and aggregated on the server to update the
master model then redistributed to the clients. In this paradigm, the user data
never leaves the client, greatly enhancing the user' privacy, in contrast to
the traditional paradigm of collecting, storing and processing user data on a
backend server beyond the user's control. In this paper we introduce, as far as
we are aware, the first federated implementation of a Collaborative Filter. The
federated updates to the model are based on a stochastic gradient approach. As
a classical case study in machine learning, we explore a personalized
recommendation system based on users' implicit feedback and demonstrate the
method's applicability to both the MovieLens and an in-house dataset. Empirical
validation confirms a collaborative filter can be federated without a loss of
accuracy compared to a standard implementation, hence enhancing the user's
privacy in a widely used recommender application while maintaining recommender
performance.

本文介绍了一个基于随机梯度方法的联邦协作过滤模型，并将其应用于用户推荐系统，旨在提高用户隐私保护，且实验表明，在联邦实现中，相较于标准实现而言，推荐系统的性能并未降低。

面向隐私保护的联邦协作过滤个性化推荐系统

Federated Collaborative Filtering for Privacy-Preserving Personalized  Recommendation System

Interactive news recommendation has been launched and attracted much
attention recently. In this scenario, user's behavior evolves from single click
behavior to multiple behaviors including like, comment, share etc. However,
most of the existing methods still use single click behavior as the unique
criterion of judging user's preferences. Further, although heterogeneous graphs
have been applied in different areas, a proper way to construct a heterogeneous
graph for interactive news data with an appropriate learning mechanism on it is
still desired. To address the above concerns, we propose a graph-based
behavior-aware network, which simultaneously considers six different types of
behaviors as well as user's demand on the news diversity. We have three main
steps. First, we build an interaction behavior graph for multi-level and
multi-category data. Second, we apply DeepWalk on the behavior graph to obtain
entity semantics, then build a graph-based convolutional neural network called
G-CNN to learn news representations, and an attention-based LSTM to learn
behavior sequence representations. Third, we introduce core and coritivity
features for the behavior graph, which measure the concentration degree of
user's interests. These features affect the trade-off between accuracy and
diversity of our personalized recommendation system. Taking these features into
account, our system finally achieves recommending news to different users at
their different levels of concentration degrees.

本研究提出了一种基于行为感知的图形网络，同时考虑六种不同类型的行为以及用户对新闻多样性的需求，使用 DeepWalk 提取实体语义，建立了一个基于图形的卷积神经网络来学习新闻的表示，以及一个基于关注力的 LSTM 来学习行为序列的表示，成功地将新闻推荐给不同程度集中度的不同用户。