Live Coding Spring, Kafka, Elasticsearch Personalized Search Results on Ranking and User Profile

Join us to see how we implemented boosting personalized search results and reengineered the legacy solution. Weve achieved 4060 less effort by our users to find the content theyre looking for among 40 million documents within 100200 milliseconds, including search, popularity, and personalization times. The average number of letters used in searches decreased from 9 to 4. In this livecoding session, well go over: Elasticsearch: basics, analyzers, char filters, token filters Rankingbased boosting Personalized (behaviorbased) boosting Kafka: realtime user profile generation Spring Boot: putting them all together