Hello r/machinelearning,

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This talk is my best attempt to strip down the core concepts of word2vec, and explain how the algorithm is being to power recommendation engines. The power of recent NLP models is a testament to how far we’ve come in extracting patterns from sequential data. This application of the algorithm treats other sequences of data (e.g. website click sessions, songs in user-created playlists) as sentences leading us to create embeddings (for items in an ecommerce store, or songs/artists in a music service) that we can use for similarity and recommendation. My Illustrated Word2vec post built on the materials I created for this talk. I hope you find it useful:

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https://www.infoq.com/presentations/nlp-word-embedding