Neural Networks for Neural Search

Search has always been a crucial part of all information systems; getting the right information to the right user is integral. This is because a user query, as in a set of keywords, cannot fully represent a user’s information needs. Traditionally, symbolic search has been developed to allow users to perform keyword-based searches. However, such search applications were bound to a text-based search box. With the recent developments in deep learning and artificial intelligence, we can encode any kind of data into vectors and measure the similarities between two vectors. This allows users to create a query with any kind of data and get any kind of search result.

In this chapter, we will review important concepts regarding information retrieval and neural search, as well as looking at the benefits that neural search provides to developers. Before we start introducing neural search, we will first introduce the drawbacks of the traditional symbolic-based search. Then, we’ll move on to looking at how to use neural networks in order to build a cross/multi-modality search. This will include looking at its major applications.

In this chapter, we’re going to cover the following main topics in particular:

• Legacy search versus neural search
• Machine learning for search
• Practical applications for neural search