lyft2vec — Embeddings at Lyft
Graph learning methods can reveal interesting insights that capture the underlying relational structures. Graph learning methods have many industry applications in areas such as product or content recommender systems and network analysis.
In this post, we discuss how we use graph learning methods at Lyft to generate embeddings — compact vector representation of high-dimensional information. We will share interesting rideshare insights uncovered by embeddings of riders, drivers, locations, and time. As the examples will show, trained embeddings from graphs can represent information and patterns that are hard to capture with traditional, straightforward features.