面向MSX销售人员的细粒度大规模内容推荐
Fine-grained large-scale content recommendations for MSX sellers
AI总结：
针对微软销售人员的商机培育需求，本文提出细粒度商机级内容推荐模型，通过语义匹配与架构优化从约4万篇内容中为每个商机推荐Top5相关内容，并结合专家与LLM评判验证推荐质量。
摘要：
微软销售人员的核心任务之一是通过主动接洽与定制化方案，细致追踪并培育潜在商业机会。推荐系统在助力销售人员达成目标的过程中发挥着核心作用。本文提出了一种内容推荐模型，可推送多种类型的内容（技术文档、竞品对比资料、客户成功案例等），供销售人员分享给客户或用于自身学习。该模型以商机为粒度运行，这是最低的可行粒度，也是与销售人员最相关的粒度。模型基于内容元数据与精心筛选的商机属性之间的语义匹配构建。考虑到微软这类组织中销售人员管理的商机规模，我们展示了如何在海量商机-内容组合上执行高效的语义匹配。核心挑战在于，需从总计约40000篇已发布内容中，为每个商机推荐前5篇相关内容。我们通过对不同模型架构与特征选择进行全面对比，实现了这一目标。最后，我们结合人类领域专家，以及近期提出的“LLM as a judge（大语言模型作为评判者）”框架，以定量方式进一步检验了推荐质量。
英文摘要：
One of the most critical tasks of Microsoft sellers is to meticulously track and nurture potential business opportunities through proactive engagement and tailored solutions. Recommender systems play a central role to help sellers achieve their goals. In this paper, we present a content recommendation model which surfaces various types of content (technical documentation, comparison with competitor products, customer success stories etc.) that sellers can share with their customers or use for their own self-learning. The model operates at the opportunity level which is the lowest possible granularity and the most relevant one for sellers. It is based on semantic matching between metadata from the contents and carefully selected attributes of the opportunities. Considering the volume of seller-managed opportunities in organizations such as Microsoft, we show how to perform efficient semantic matching over a very large number of opportunity-content combinations. The main challenge is to ensure that the top-5 relevant contents for each opportunity are recommended out of a total of $\approx 40,000$ published contents. We achieve this target through an extensive comparison of different model architectures and feature selection. Finally, we further examine the quality of the recommendations in a quantitative manner using a combination of human domain experts as well as by using the recently proposed "LLM as a judge" framework.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
