Deep Learning for Matching in Search and Recommendation

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 learning methods have been exploited to address the problem, which learns a matching function from labeled data, also referred to as “learning to match” [17]. In recent years, deep learning has been successfully applied to matching and signi€cant progresses have been made. Deep semantic matching models for search [21] and neural collaborative €ltering models for recommendation [9] are becoming the state-of-the-art technologies. ‘e key to the success of the deep learning approach is its strong ability in learning of representations and generalization of matching pa‹erns 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 learning for matching in search and recommendation. Our tutorial is unique in that we try to give a uni€ed view on search and recommendation. In this way, we expect researchers from the two €elds can get deep understanding and accurate insight on the spaces, stimulate more ideas and discussions, and promote developments of technologies. ‘e 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 learning techniques are utilized to address the matching problem in search and recommendation. Lastly, we elaborate how deep learning can be e‚ectively used to solve the matching problems in both tasks.