The modern architecture of search

Information Retrieval (IR) systems are a vital component in the core of successful modern web platforms.

The main goal of IR systems is to provide a communication layer that enables customers to establish a retrieval dialogue with underlying data.

The immense explosion of unstructured data drives modern search application to go beyond just fuzzy string matching, to invest in deep understanding of user queries through interpretation of user intention in order to respond with a relevant result set.

The modern architecture of search is a design of a data-driven IR system that covers the following:

- Data ingestion pipelines from various sources

- Data retrieval and the lifecycle of a user search query

- Machine learned relevance ranking

- Personalised search

- Search performance tracking and quality assessment

The talk will discuss the components needed to build an eco-system that is designed to solve the problems of IR in web platforms. What role can Machine learning play in search relevancy? how natural language processing can help provide a solid understanding of search phrases? How data can drive a personalized search experience? What are the challenges of maintaining such a complex system?