2024-06-10 –, Palais Atelier
At mobile.de we are striving to provide a better and faster search. We use a backend ML system to learn changing user interests and optimise search experience. Based on learning to rank using XGBoost, we discuss current search relevance ranking framework and how it ranks millions of searches daily.
At mobile.de, we continuously strive to provide our users with a better, faster and a unique search experience. Machine learning and Python play a key role in providing this experience.
Every day, millions of people visit mobile.de to find their dream car. The user journey typically starts by entering a search query and later refining it based on their requirements. If the user finds a relevant listing, they contact the seller to purchase the vehicle. Our search engine is responsible for matching users with the right sellers.
In this talk, I will talk about:
- Introduction
- Why search is important
- How learning to rank helps ?
- Current challenges with our ranking models
- Proposed solution
- How do we deploy our ranking models ? (Under strict latency SLA <30ms)
- AB Test results
- Key Learnings
- How can we improve further
This session is sponsored by mobile.de
Manish is currently working as a Senior Data Scientist with a strong focus on building, deploying and serving models. With over nine years working on machine learning problems, he really enjoys building data products around improving search, ranking and recommendations. Outside work, he likes to do outdoor activities like running, swimming etc.