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...