Ruining He | Pinterest engineer, Pinterest Labs Deep learning methods have achieved unprecedented performance on a broad range of machine learning and artificial intelligence tasks like visual recognition, speech recognition and machine translation. However, despite amazing progress, deep learning research has mainly focused on data defined on Euclidean domains, such as grids (e.g., images) and sequences (e.g., speech, text).

Nonetheless, most interesting data, and challenges, are defined on non-Euclidean domains such as graphs, manifolds and recommender systems. The main question is, how to define basic deep learning operations for such complex data types. With a growing and global service, we don’t have the option of a system that won’t scale for everyday use.

Our answer came in the form of PinSage, a random-walk Graph Convolutional Network capable of learning embeddings for nodes in web-scale graphs containing billions of objects. Here we’ll show how we can create high-quality embeddings (i.e.

dense vector representations) of nodes (e.g., Pins/images) connected into a large graph. The benefit of our approach is that by borrowing information from nearby nodes/Pins the resulting embedding of a node becomes more accurate and more robust.

For example, a bed rail Pin might look like a garden fence, but gates and beds are rarely adjacent in the graph. Our model relies on this graph information to provide the context and allows us to disambiguate Pins that are (visually) similar, but semantically different. Read more from…

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