Summary
In this chapter, you have learned about the key concepts of searching and matching. We have also covered the difference between legacy search and neural-network-based search. We saw how neural networks can help us tackle the issues traditional search cannot solve, such as cross-modality or multi-modality search.
Neural networks are able to encode different types of information into a common embedding space and make different pieces of information comparable, and that’s why deep learning and neural networks have the potential to better fulfill a user’s information needs.
We have introduced several possible applications using deep-learning-powered search systems, for instance, vision-based product search in fashion or tourism, or text-based search for question answering and text deduplication. More kinds of application are still to be explored!
You should now understand the core idea behind neural search: neural search has the ability to encode any kind of data into an expressive representation, namely an embedding. Creating a quality embedding is crucial to a search application powered by deep learning, since it determines the quality of the final search result.
In the next chapter, we will introduce the foundations of embeddings, such as how to encode information into embeddings, how to measure the distance between different embeddings, and some of the most important models we can use to encode different modalities of data.
