Latent user representations are widely adopted in the tech industry for powering personalized recommender systems. Most prior work infers a single high dimensional embedding to represent a user, which is a good starting point but falls short in delivering a full understanding of the user's interests. In this work, we introduce PinnerSage, an end-to-end recommender system that represents each user via multi-modal embeddings and leverages this rich representation of users to provides high quality personalized recommendations. PinnerSage achieves this by clustering users' actions into conceptually coherent clusters with the help of a hierarchical clustering method (Ward) and summarizes the clusters via representative pins (Medoids) for efficiency and interpretability. PinnerSage is deployed in production at Pinterest and we outline the several design decisions that makes it run seamlessly at a very large scale. We conduct several offline and online A/B experiments to show that our method significantly outperforms single embedding methods.

中文翻译：

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PinnerSage：Pinterest 推荐的多模式用户嵌入框架

潜在用户表示在技术行业中被广泛采用，以支持个性化推荐系统。大多数先前的工作推断出一个单一的高维嵌入来表示用户，这是一个很好的起点，但未能充分了解用户的兴趣。在这项工作中，我们引入了 PinnerSage，这是一个端到端的推荐系统，它通过多模态嵌入表示每个用户，并利用这种丰富的用户表示提供高质量的个性化推荐。PinnerSage 通过在层次聚类方法 (Ward) 的帮助下将用户的操作聚类到概念上一致的集群中来实现这一点，并通过代表性引脚 (Medoids) 总结集群以提高效率和可解释性。PinnerSage 已部署在 Pinterest 的生产环境中，我们概述了使其在非常大规模的情况下无缝运行的几个设计决策。我们进行了几次离线和在线 A/B 实验，以表明我们的方法明显优于单嵌入方法。