Nowadays, the rapid development of mobile economy has promoted the
flourishing of online marketing campaigns, whose success greatly hinges on the
efficient matching between user preferences and desired marketing campaigns
where a well-established Marketing-oriented Knowledge Graph (dubbed as MoKG)
could serve as the critical "bridge" for preference propagation. In this paper,
we seek to carefully prompt a Large Language Model (LLM) with domain-level
knowledge as a better marketing-oriented knowledge miner for marketing-oriented
knowledge graph construction, which is however non-trivial, suffering from
several inevitable issues in real-world marketing scenarios, i.e.,
uncontrollable relation generation of LLMs,insufficient prompting ability of a
single prompt, the unaffordable deployment cost of LLMs. To this end, we
propose PAIR, a novel Progressive prompting Augmented mIning fRamework for
harvesting marketing-oriented knowledge graph with LLMs. In particular, we
reduce the pure relation generation to an LLM based adaptive relation filtering
process through the knowledge-empowered prompting technique. Next, we steer
LLMs for entity expansion with progressive prompting augmentation,followed by a
reliable aggregation with comprehensive consideration of both self-consistency
and semantic relatedness. In terms of online serving, we specialize in a small
and white-box PAIR (i.e.,LightPAIR),which is fine-tuned with a high-quality
corpus provided by a strong teacher-LLM. Extensive experiments and practical
applications in audience targeting verify the effectiveness of the proposed
(Light)PAIR.

现在，移动经济的快速发展推动了在线营销活动的繁荣，其成功在很大程度上取决于用户偏好和所需营销活动之间的有效匹配，而良好建立的以营销为导向的知识图谱（称为 MoKG）可以成为偏好传播的关键 “桥梁”。本文旨在将领域级知识作为更好的以营销为导向的知识挖掘器，用于以 LLM 为基础的营销导向的知识图谱构建，然而在现实世界的营销场景中，这不是一件容易的事，存在着一些不可避免的问题，即 LLM 的不可控关系生成、单一提示的能力不足以及 LLM 的部署成本过高。为此，我们提出了一种新颖的渐进提示增强挖掘框架（PAIR），用于获取具有 LLM 的营销导向的知识图谱。具体而言，通过知识增强提示技术，我们将纯关系生成过程转化为基于 LLM 的自适应关系过滤过程。接下来，我们通过渐进提示增强引导 LLM 进行实体扩展，然后通过充分考虑自我一致性和语义相关性进行可靠的聚合。在在线服务方面，我们专注于一个小型的白盒 PAIR（即 LightPAIR），它是通过高质量语料库由强大的 Teacher-LLM 进行微调的。广泛的实验和在受众定位方面的实际应用验证了所提出的（Light）PAIR 的有效性。

运用渐进式提示增强，使大型语言模型成为在线营销的更好知识挖掘工具

Making Large Language Models Better Knowledge Miners for Online  Marketing with Progressive Prompting Augmentation

We study the budget allocation problem in online marketing campaigns that
utilize previously collected offline data. We first discuss the long-term
effect of optimizing marketing budget allocation decisions in the offline
setting. To overcome the challenge, we propose a novel game-theoretic offline
value-based reinforcement learning method using mixed policies. The proposed
method reduces the need to store infinitely many policies in previous methods
to only constantly many policies, which achieves nearly optimal policy
efficiency, making it practical and favorable for industrial usage. We further
show that this method is guaranteed to converge to the optimal policy, which
cannot be achieved by previous value-based reinforcement learning methods for
marketing budget allocation. Our experiments on a large-scale marketing
campaign with tens-of-millions users and more than one billion budget verify
the theoretical results and show that the proposed method outperforms various
baseline methods. The proposed method has been successfully deployed to serve
all the traffic of this marketing campaign.

提出一种基于值函数的强化学习方法来解决在线营销活动中利用离线数据进行预算分配的问题，该方法通过使用混合策略减少存储策略的数量，并实现了接近最优策略的效率，经过大规模的营销活动实验证明该方法优于其他基准方法。