This survey examines the most effective retrieval algorithms utilized in ad
recommendation and content recommendation systems. Ad targeting algorithms rely
on detailed user profiles and behavioral data to deliver personalized
advertisements, thereby driving revenue through targeted placements.
Conversely, organic retrieval systems aim to improve user experience by
recommending content that matches user preferences. This paper compares these
two applications and explains the most effective methods employed in each.

本调查论文比较了广告推荐系统和内容推荐系统中最有效的检索算法，广告目标算法依赖于详细的用户配置文件和行为数据来提供个性化广告，从而通过定向投放来推动收益。相反，有机检索系统旨在通过推荐符合用户偏好的内容来改善用户体验。

广告和内容推荐系统中的检索算法调研

A Survey of Retrieval Algorithms in Ad and Content Recommendation  Systems

We propose a new method to measure the task-specific accuracy of
Retrieval-Augmented Large Language Models (RAG). Evaluation is performed by
scoring the RAG on an automatically-generated synthetic exam composed of
multiple choice questions based on the corpus of documents associated with the
task. Our method is an automated, cost-efficient, interpretable, and robust
strategy to select the optimal components for a RAG system. We leverage Item
Response Theory (IRT) to estimate the quality of an exam and its
informativeness on task-specific accuracy. IRT also provides a natural way to
iteratively improve the exam by eliminating the exam questions that are not
sufficiently informative about a model's ability. We demonstrate our approach
on four new open-ended Question-Answering tasks based on Arxiv abstracts,
StackExchange questions, AWS DevOps troubleshooting guides, and SEC filings. In
addition, our experiments reveal more general insights into factors impacting
RAG performance like size, retrieval mechanism, prompting and fine-tuning. Most
notably, our findings show that choosing the right retrieval algorithms often
leads to bigger performance gains than simply using a larger language model.

我们提出了一种新的方法来衡量检索增强的大型语言模型（RAG）的任务特定准确性。通过对与任务相关的文档语料库基于多项选择问题评分的自动生成合成考试来进行评估。我们的方法是自动化、成本高效、可解释和稳健的选择 RAG 系统的最佳组件的策略。我们利用项目反应理论（IRT）估计考试的质量和信息量，以提高任务特定准确性。我们在四个基于 Arxiv 摘要、StackExchange 问题、AWS DevOps 故障排除指南和 SEC 文件的新型开放问答任务上演示了我们的方法。此外，我们的实验揭示了影响 RAG 性能的更一般的因素，如大小、检索机制、提示和微调。最重要的是，我们的研究结果表明，选择正确的检索算法通常比仅仅使用更大的语言模型能够带来更大的性能收益。