Information Retrieval (IR) systems are a vital component in the core of successful modern web platforms, and Zalando understand their importance incredibly well.

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 applications 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
• Personalized search
• Search performance tracking and quality assessment

At the recent Berlin Buzzwords conference this month, we discussed the components needed to build an ecosystem that is designed to solve the problems of IR in web platforms. What role can Machine Learning play in search relevancy? How can natural language processing help provide a solid understanding of search phrases? How can data drive a personalized search experience? And finally, what are the challenges of maintaining such a complex system?

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