In this paper, we explore a new way for user targeting, where non-expert
marketers could select their target users solely given demands in natural
language form. The key to this issue is how to transform natural languages into
practical structured logical languages, i.e., the structured understanding of
marketer demands. Considering the impressive natural language processing
ability of large language models (LLMs), we try to leverage LLMs to solve this
issue. Past research indicates that the reasoning ability of LLMs can be
effectively enhanced through chain-of-thought (CoT) prompting. But existing
methods still have some limitations: (1) Previous methods either use simple
"Let's think step by step" spells or provide fixed examples in demonstrations
without considering compatibility between prompts and questions, making LLMs
ineffective in some complex reasoning tasks such as structured language
transformation. (2) Previous methods are often implemented in closed-source
models or excessively large models, which is not suitable in industrial
practical scenarios. Based on these, we propose ARALLM (i.e., Analogical
Reasoning Augmented Large Language Models) consisting of two modules:
Analogical Reasoning based Prompting and Reasoning-Augmented Multi-Task Model
Distillation.

我们提出了 ARALLM（即类比推理增强的大型语言模型），包括两个模块：基于类比推理的提示和推理增强多任务模型蒸馏。

更好地了解您的需求：利用类比推理增强的结构化市场营销人员需求理解模型

Know Your Needs Better: Towards Structured Understanding of Marketer  Demands with Analogical Reasoning Augmented LLMs

When doing private domain marketing with cloud services, the merchants
usually have to purchase different machine learning models for the multiple
marketing purposes, leading to a very high cost. We present a unified user-item
matching framework to simultaneously conduct item recommendation and user
targeting with just one model. We empirically demonstrate that the above
concurrent modeling is viable via modeling the user-item interaction matrix
with the multinomial distribution, and propose a bidirectional bias-corrected
NCE loss for the implementation. The proposed loss function guides the model to
learn the user-item joint probability $p(u,i)$ instead of the conditional
probability $p(i|u)$ or $p(u|i)$ through correcting both the users and items'
biases caused by the in-batch negative sampling. In addition, our framework is
model-agnostic enabling a flexible adaptation of different model architectures.
Extensive experiments demonstrate that our framework results in significant
performance gains in comparison with the state-of-the-art methods, with greatly
reduced cost on computing resources and daily maintenance.

通过模型对用户 - 商品的互动矩阵进行建模，并提出了一个修正双向偏差的损失函数，我们提出了一个统一的用户 - 商品匹配框架，以实现商品推荐和用户定位，深度减少了计算资源和日常维护成本，从而证明了我们的框架相对于现有方法的显著性能提升。

UniMatch: 多用途商家营销的统一用户 - 商品匹配框架

UniMatch: A Unified User-Item Matching Framework for the Multi-purpose  Merchant Marketing

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。