04-22, 11:25–12:10 (Europe/Berlin), B09
At mobile.de, we aim to provide a satisfactory search experience so users can find the vehicles quickly they are looking for. We make it happen using our machine learning systems working 24X7 in the backend which continuously learns changing user interests and optimize the search experience. Based on techniques like learning to rank using XGBoost, this talk will discuss our 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 plays 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 we deploy our ranking models ? (Under strict latency SLA <30ms)
- AB Test results
- Key Learnings
- How can we improve further
Intermediate
Expected audience expertise: Python –Intermediate
Abstract as a tweet (X) or toot (Mastodon) –This talk will discuss our current search relevance ranking framework and how it ranks millions of searches daily.
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.