Many real-world machine learning problems can be framed as graph problems. On online platforms, users often share assets (e.g. photos) and interact wi

Graph Machine Learning at Airbnb

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2022-07-01 19:00:08

Many real-world machine learning problems can be framed as graph problems. On online platforms, users often share assets (e.g. photos) and interact with each other (e.g. messages, bookings, reviews). These connections between users naturally form edges that can be used to create a graph.

However, in many cases, machine learning practitioners do not leverage these connections when building machine learning models, and instead treat nodes (in this case, users) as completely independent entities. While this does simplify things, leaving out information around a node’s connections may reduce model performance by ignoring where this node is in the context of the overall graph.

In this blog post, we will explain the benefits of using graphs for machine learning, and show how leveraging graph information allows us to learn more about our users, in addition to building more contextual representations of them [4]. We will then cover specific graph machine learning methods, such as Graph Convolutional Networks, that are being used at Airbnb to improve upon existing machine learning models.

The motivating use-case for this work is to build machine learning models that protect our community from harm, but many of the points being made and systems being built are quite generic and could be applied to other tasks as well.

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