In this work, we present our journey to revolutionize the personalized
recommendation engine through end-to-end learning from raw user actions. We
encode user's long-term interest in Pinner- Former, a user embedding optimized
for long-term future actions via a new dense all-action loss, and capture
user's short-term intention by directly learning from the real-time action
sequences. We conducted both offline and online experiments to validate the
performance of the new model architecture, and also address the challenge of
serving such a complex model using mixed CPU/GPU setup in production. The
proposed system has been deployed in production at Pinterest and has delivered
significant online gains across organic and Ads applications.

本研究旨在通过端到端的学习，从原始用户行为中进行个性化推荐引擎的革新，通过一种新的密集全行动损失，编码用户的长期兴趣来优化用户嵌入，通过直接学习实时操作序列来捕获用户的短期意图。通过离线和在线实验验证了新模型架构的性能，并解决了在生产中使用混合 CPU/GPU 设置服务这种复杂模型的挑战。

Pinterest 个性化排名的全流程重新思考方法

Rethinking Personalized Ranking at Pinterest: An End-to-End Approach

Online forums and social media platforms provide noisy but valuable data
every day. In this paper, we propose a novel end-to-end neural network-based
user embedding system, Author2Vec. The model incorporates sentence
representations generated by BERT (Bidirectional Encoder Representations from
Transformers) with a novel unsupervised pre-training objective, authorship
classification, to produce better user embedding that encodes useful
user-intrinsic properties. This user embedding system was pre-trained on post
data of 10k Reddit users and was analyzed and evaluated on two user
classification benchmarks: depression detection and personality classification,
in which the model proved to outperform traditional count-based and
prediction-based methods. We substantiate that Author2Vec successfully encoded
useful user attributes and the generated user embedding performs well in
downstream classification tasks without further finetuning.

本文提出了一种基于神经网络和 BERT 模型的用户嵌入系统 ——Author2Vec，其通过使用 BERT 模型中的句子嵌入技术，结合新颖的无监督预训练目标，即作者分类，以生成更好的用户嵌入表示。该系统在 Reddit 平台的 1 万个用户的文章数据上进行预训练，并在抑郁症与人格分类等两个用户分类基准中进行了分析和评估，结果表明其性能优于传统的基于计数和基于预测的方法。Author2Vec 成功地编码了有用的用户属性，并在下游分类任务中表现出良好的性能，无需进一步的微调。