Rakuten Technology Conference 2017

Over the past decade, e-commerce has leaped from lighthouse customers to mainstream consumers, offering online inventories with millions of products readily available to shoppers. To help buyers easily find products and fulfill their goals, it is necessary to provide effective search methods that retrieve highly-relevant items. However, manual and/or rule-based approaches to search optimization are not scalable. In this talk, we illustrate machine learning methods that have been successfully applied at web-scale to optimize search relevance for e-commerce. Additionally, we present techniques to extract semantic information from queries and precisely match product attributes to improve search relevance against structured products.