Deep Neural Networks for Search and Recommendation Systems at LinkedIn
Deep neural networks like convolutional neural networks (CNN), recurrent neural networks (RNN), and attention-based encoder-decoder networks have made a big impact in several natural language processing (NLP) applications, such as sentence classification, part of speech tagging, and machine translation. In recent years, models like BERT and its variants have improved the state of the art in NLP through contextual word embeddings, and sentence embeddings. Another attraction of these models is that they can be finetuned for target applications.
In this talk, I will describe how we have successfully used deep neural networks for natural language processing and understanding at LinkedIn. In particular, I will discuss our work in query and document understanding, as well as document ranking for search and recommendation systems.
Ananth Sankar is a Principal Staff Engineer in the Artificial Intelligence group at LinkedIn, where he works on multimedia content understanding and natural language processing. During his career, he has also made many R&D contributions in the area of speech recognition. He has taught courses at Stanford and UCLA, given several invited talks, co-authored more than 50 refereed publications, and has 10 accepted patents.