Explore the application of text embedding algorithms in recommendation systems through this 44-minute conference talk from WeAreDevelopers. Discover how word2vec outperforms state-of-the-art models in various recommendation tasks, offering particular value for practitioners dealing with large-scale user and item data in online shop settings. Delve into topics such as NLP, voice assistants, man-machine communication, and the bag of words model. Learn about the SIBO architecture, collaborative vs. content-based filtering, and practical problem-solving approaches. Gain insights from Simon Stiebellehner's presentation, which covers the surprising efficiency of word2vec in recommendation systems and its applications beyond typical NLP problems.