论文标题
Pinnersage：Pinterest上的建议的多模式用户嵌入框架
PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest
论文作者
论文摘要
潜在用户表示在科技行业中广泛采用，用于为个性化的推荐系统供电。大多数先前的工作都渗透了一个高维嵌入以代表用户，这是一个很好的起点，但在对用户兴趣的充分了解方面缺乏。在这项工作中，我们介绍了Pinnersage，这是一种端到端的推荐系统，该系统通过多模式嵌入来代表每个用户，并利用这种丰富的用户表示来提供高质量的个性化建议。 Pinnersage通过借助层次聚类方法（Ward）将用户的动作聚集到概念上连贯的群集中，从而实现这一目标，并通过代表性引脚（Medoids）汇总簇的效率和解释性。 Pinnersage在Pinterest的生产中部署，我们概述了几项设计决策，这些决策使其在非常大的规模上无缝运行。我们进行了多个离线和在线A/B实验，以表明我们的方法明显优于单个嵌入方法。
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.
