Deep Transfer Learning for Search and Recommendation

Abstract

Training data sparsity is a common problem for many real-world applications in Search and Recommendation domains. Even for applications with a lot of training data, in the cold-start scenario we usually do not get enough labeled data. Transfer Learning is a promising approach for addressing this problem. In addition, features might interact with each other in a complex way that traditional approaches might not be able to represent, Deep Transfer Learning, which leverages Deep Neural Networks for Transfer Learning, might be able to catch such deep patterns hidden in complex feature interactions. Due to these reasons, recently Deep Transfer Learning research has gained a lot of attention and has been successfully applied to many real-world applications. This tutorial offers an overview of Deep Transfer Learning approaches in Search and Recommendation domains from the industry perspective. In this tutorial We first introduce the basic concepts and major categories of Deep Transfer Learning. Then we focus on recent developments of Deep Transfer Learning approaches in the Search and Recommendation domains. After that we will introduce two real-world examples of how to apply Deep Transfer Learning methods to improve Search and Recommendation performance at LinkedIn. Finally we will conclude the tutorial with discussion of future directions.