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.
Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time
Pinterest
现代内容发现应用中的用户体验关键取决于高质量的个性化推荐。然而，由于大量的项目池、大量的用户以及对响应于用户动作并按需实时的【形】生成的推荐的要求，构建提供这种推荐的系统提出了重大挑战。在这里，我们介绍了Pixie，一个可扩展的基于图的实时的【形】推荐系统，我们开发和部署在Pinterest。给定一组用户特定的引脚作为查询，Pixie会从数十亿个可能的引脚中实时的【形】选择与查询最相关的引脚。为了生成推荐，我们开发了Pixie Random Walk算法，该算法利用了30亿个节点和170亿条边的Pinterest对象图。实验表明，与以前基于Hadoop的生产系统相比，Pixie提供的推荐可以提高50%的用户参与度。此外，我们还开发了一种图修剪策略，使推荐性能提高了58%。最后，我们讨论Pixie的系统方面，其中单个服务器每秒执行1，200个推荐请求，延迟为60毫秒。今天，Pixie支持的系统贡献了Pinterest上超过80%的用户参与度。
