Using Text Embedding Algorithms in Recommender Systems

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.

Introduction
About the company
Topics
NLP
Voice Assistants
ManMachine Communication
Natural Language Processing
Bag of Words Model
Two Solution Hypothesis
SIBO Architecture
Collaborative vs ContentBased Filtering
rhetorical question
perspective
sequences of words
Aggregation
Notebook
Data
Recommendations
Problems
Summary