Using neural embeddings for search ranking and recommendations
This is a summary of a project that I worked on while working as a data scientist / software engineer at Peerspace . Peerspace is a two-sided marketplace for renting unique spaces for meetings, events, off-sites and things like that. It's basically like Airbnb, but leaning more towards commercial use cases and hourly rentals. A screenshot of what the Peerspace search results page looks like While I was at Peerspace, I had the opportunity to work on alot of pretty interesting things. This included learning Clojure, which is a dynamically typed Lisp dialect which runs on the JVM. In addition, I got to do a lot of work with search ranking and search infrastructure. One of the search-related projects I worked on was exploring and applying deep learning to improve our search ranking and recommendations. One algorithm I explored was called product2vec . which essentially uses deep learning to learn a neural "embedding" for each of the products in a product ...