DoorDash is a food delivery company where users find restaurants to order from. When a user opens the DoorDash app, the user can search for types of food or specific restaurants from the search bar or they can scroll through the feed section and look at recommendations that the app gives them within their local geographic area.

Recommendations is a classic computer science problem. Much like sorting, or mapping, or scheduling, we will probably never “solve” recommendations. We will adapt our recommendation systems based off of discoveries in computer science and software engineering.

One pattern that has been utilized recently by software engineers in many different areas is the “word2vec”-style strategy of embedding entities in a vector space and then finding relationships between them. If you have never heard of the word2vec algorithm, you can listen to the episode we did with computer scientist and venture capitalist Adrian Colyer or listen to this episode in which we will describe the algorithm with a few brief examples.

Store2vec is a strategy used by DoorDash to model restaurants in vector space and find relationships between them in order to generate recommendations. Mitchell Koch is a senior data scientist with DoorDash, and he joins the show to discuss the application of store2vec, and the more general strategy of word2vec-like systems. This episode is also a great companion to our episode about data infrastructure at DoorDash.

Show Notes

• Medium – Personalized Store Feed with Vector Embeddings
• Medium – DoorDash
• Skymind AI – A Beginner’s Guide to Word2Vec

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