Pinterest如何利用推荐中的实时用户行为来提高Homefeed的参与量
Xue Xia, Software Engineer, Homefeed Ranking; Neng Gu, Software Engineer, Content & User Understanding; Dhruvil Deven Badani, Engineering Manager, Homefeed Ranking; Andrew Zhai, Software Engineer, Advanced Technologies Group
Xue Xia，软件工程师，Homefeed排名；Neng Gu，软件工程师，内容与用户理解；Dhruvil Deven Badani，工程经理，Homefeed排名；Andrew Zhai，软件工程师，先进技术组
Image from https://wallpapercave.com/neural-networks-wallpapers#google_vignette
图片来自https://wallpapercave.com/neural-networks-wallpapers#google_vignette
In this blog post, we will demonstrate how we improved Pinterest Homefeed engagement volume from a machine learning model design perspective — by leveraging realtime user action features in Homefeed recommender system.
在这篇博文中，我们将展示我们如何从机器学习模型设计的角度改善Pinterest Homefeed的参与量--通过利用Homefeed推荐系统中的实时用户行动特征。
Background
背景介绍
The Homepage of Pinterest is the one of most important surfaces for pinners to discover inspirational ideas and contributes to a large fraction of overall user engagement. The pins shown in the top positions on the Homefeed need to be personalized to create an engaging pinner experience. We retrieve a small fraction of the large volume of pins created on Pinterest as Homefeed candidate pins, according to user interest, followed boards, etc. To present the most relevant content to pinners, we then use a Homefeed ranking model (aka Pinnability model) to rank the retrieved candidates by accurately predicting their personalized relevance to given users. Therefore, the Homefeed ranking model plays an important role in improving pinner experience. Pinnability is a state-of-the-art neural network model that consumes pin signals, user signals, context signals, etc. and predicts user action given a pin. The high level architecture is shown in Figure 3.
Pinterest的主页是Pinners发现灵感的最重要的界面之一，并在整个用户参与度中占了很大一部分。显示在主页上最重要位置的图钉需要个性化，以创造一个吸引人的品客体验。我们从Pinterest上创建的大量图钉中提取一小部分作为Homefeed候选图钉，根据用户的兴趣、关注的板块等。为了向品客展示最相关的内容，我们使用一个Homefeed排名模型（又称品客模型），通过准确预测其与特定用户的个性化相关性，对检索到的候选品进行排名。因此，Homefeed排名模型在改善品客体验方面发挥着重要作用。Pinnability是一个最先进的神经网络模型，它消耗针脚信号、用户信号、上...