With the growing popularity of various mobile devices, user targeting has
received a growing amount of attention, which aims at effectively and
efficiently locating target users that are interested in specific services.
Most pioneering works for user targeting tasks commonly perform
similarity-based expansion with a few active users as seeds, suffering from the
following major issues: the unavailability of seed users for newcoming services
and the unfriendliness of black-box procedures towards marketers. In this
paper, we design an Entity Graph Learning (EGL) system to provide explainable
user targeting ability meanwhile applicable to addressing the cold-start issue.
EGL System follows the hybrid online-offline architecture to satisfy the
requirements of scalability and timeliness. Specifically, in the offline stage,
the system focuses on the heavyweight entity graph construction and user entity
preference learning, in which we propose a Three-stage Relation Mining
Procedure (TRMP), breaking loose from the expensive seed users. At the online
stage, the system offers the ability of user targeting in real-time based on
the entity graph from the offline stage. Since the user targeting process is
based on graph reasoning, the whole process is transparent and
operation-friendly to marketers. Finally, extensive offline experiments and
online A/B testing demonstrate the superior performance of the proposed EGL
System.

本文提出了一种实体图学习（EGL）系统，采用混合的在线 - 离线架构，通过离线重视实体图构建和用户实体偏好学习，以及在线实时用户定向能力，解决冷启动问题，实现用户定向，实验表明提出的 EGL 系统具有优异的性能。

一个实体图学习系统用于用户定向服务兴趣推荐

Who Would be Interested in Services? An Entity Graph Learning System for  User Targeting

In the information explosion era, recommender systems (RSs) are widely
studied and applied to discover user-preferred information. A RS performs
poorly when suffering from the cold-start issue, which can be alleviated if
incorporating Knowledge Graphs (KGs) as side information. However, most
existing works neglect the facts that node degrees in KGs are skewed and
massive amount of interactions in KGs are recommendation-irrelevant. To address
these problems, in this paper, we propose Differentiable Sampling on Knowledge
Graph for Recommendation with Relational GNN (DSKReG) that learns the relevance
distribution of connected items from KGs and samples suitable items for
recommendation following this distribution. We devise a differentiable sampling
strategy, which enables the selection of relevant items to be jointly optimized
with the model training procedure. The experimental results demonstrate that
our model outperforms state-of-the-art KG-based recommender systems. The code
is available online at this https URL

本文提出了基于知识图谱的可区分采样技术（DSKReG），用于解决推荐系统中的冷启动问题，并在实验中证明了其优于现有技术的表现。

DSKReG：可微分的知识图谱采样与关系图神经网络推荐

DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation  with Relational GNN

Sequential Recommendation characterizes the evolving patterns by modeling
item sequences chronologically. The essential target of it is to capture the
item transition correlations. The recent developments of transformer inspire
the community to design effective sequence encoders, \textit{e.g.,} SASRec and
BERT4Rec. However, we observe that these transformer-based models suffer from
the cold-start issue, \textit{i.e.,} performing poorly for short sequences.
Therefore, we propose to augment short sequences while still preserving
original sequential correlations. We introduce a new framework for
\textbf{A}ugmenting \textbf{S}equential \textbf{Re}commendation with
\textbf{P}seudo-prior items~(ASReP). We firstly pre-train a transformer with
sequences in a reverse direction to predict prior items. Then, we use this
transformer to generate fabricated historical items at the beginning of short
sequences. Finally, we fine-tune the transformer using these augmented
sequences from the time order to predict the next item. Experiments on two
real-world datasets verify the effectiveness of ASReP. The code is available on
https://github.com/DyGRec/ASReP.

本研究提出了一个新的框架 ASReP，其目的是增强短序列数据的特征，避免 transformer-based models 在冷启动问题上性能不佳。通过预训练将 transformer 引导生成历史虚拟数据，并在时间顺序上微调 transformer。实验证明 ASReP 的应用有效性。