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 re-engineered the legacy solution.
We’ve achieved 40%-60% less effort by our users to find the content they’re looking for among 40 million documents within 100-200 milliseconds, including search, popularity, and personalization times. The average number of letters used in searches decreased from 9 to 4.
In this live-coding session, we’ll go over:
Elasticsearch: basics, analyzers, char filters, token filters
Ranking-based boosting
Personalized (behavior-based) boosting
Kafka: real-time user profile generation
Spring Boot: putting them all together
Erdem Günay, CTO at Layermark
Slides: www.slideshare...

[@mcandan]

Title might misleading but content is great. Clarified all my questions about ranking based on user profile. Thanks a lot

[@fahadziasyed4568]

Very informative for Elastic Search enthusiasts.

[@Strannik20111]

Actually it was not a live coding but rather live code demonstration) And Kafka was talked only 3 minutes about ....

[@haroldpepete]

what is the purpose of this talk, one guy showing a lot of code with no reason, what a miss