Word Embeddings in Search
One of the main innovations rediscovered in the last years in search and machine learning is the concept of Embeddings. In search, embeddings have been used in many different applications including retrieval, advertising, and recommender systems. In this talk we are going to show some applications of vector space embeddings that have considerably improved the state of the art. All the applications shown have been adopted by main search companies in production in their systems.
Fabrizio Silvestri is a Software Engineer at Facebook London in the Search Systems team. His interests are in web search in general and in particular his specialization is building systems to better interpret queries from search users. Prior to Facebook, Fabrizio was a principal scientist at Yahoo where he has worked on sponsored search and native ads within the Gemini project. Fabrizio holds a Ph.D. in Computer Science from the University of Pisa, Italy where he studied problems related to Web Information Retrieval with particular focus on Efficiency related problems like Caching, Collection Partitioning, and Distributed IR in general.