Matching is the key problem in both search and recommendation, that is to measure the relevance of a document to a query or the interest of a user on an item. Previously, machine methods have been exploited to address the problem, which learns a matching function from labeled data, also referred to as learning to match''. In recent years, deep has been successfully applied to matching and significant progresses have been made. Deep semantic matching models for search and neural collaborative filtering models for recommendation are becoming the state-of-the-art technologies. The key to the success of the deep approach is its strong ability in of representations and generalization of matching patterns from raw data (e.g., queries, documents, users, and items, particularly in their raw forms). In this tutorial, we aim to give a comprehensive survey on recent progress in deep for matching in search and recommendation. Our tutorial is unique in that we try to give a unified view on search and recommendation. In this way, we expect researchers from the two fields can get deep understanding and accurate insight on the spaces, stimulate more ideas and discussions, and promote developments of technologies. The tutorial mainly consists of three parts. Firstly, we introduce the general problem of matching, which is fundamental in both search and recommendation. Secondly, we explain how traditional machine techniques are utilized to address the matching problem in search and recommendation. Lastly, we elaborate how deep can be effectively used to solve the matching problems in both tasks.