用用户行为和内容特征，智能排名内容提供者。
A Framework for Ranking Content Providers Using Prompt Engineering and Self-Attention Network
- 结合点击、点赞等行为与写作风格、发布频率等特征
- 利用语言模型生成提示，构建标注数据集提升排序精度
- 适合需要提升推荐内容质量与多样性的平台使用
本文针对内容推荐系统中内容提供者的排序问题提出一个框架。内容提供者是新闻及生活方式、旅行、园艺等内容的来源。该框架融合显式用户反馈（如点击与反应）和内容特征（如写作风格、发布频率），对特定主题下的内容提供者进行排序。通过语言模型设计提示词，构建了此前无监督排序任务的基准数据集。基于此数据集，采用基于自注意力机制的网络，在列表级学习排序任务上进行训练。通过线上实验评估，结果表明该框架可有效提升推荐内容的质量、可信度与多样性。
原文摘要 · Abstract (English)
This paper addresses the problem of ranking Content Providers for Content Recommendation System. Content Providers are the sources of news and other types of content, such as lifestyle, travel, gardening. We propose a framework that leverages explicit user feedback, such as clicks and reactions, and content-based features, such as writing style and frequency of publishing, to rank Content Providers for a given topic. We also use language models to engineer prompts that help us create a ground truth dataset for the previous unsupervised ranking problem. Using this ground truth, we expand with a self-attention based network to train on Learning to Rank ListWise task. We evaluate our framework using online experiments and show that it can improve the quality, credibility, and diversity of the content recommended to users.
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