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Better search relevance using Learning to Rank at mobile.de

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Better search relevance using Learning to Rank at mobile.de
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64
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CC Attribution 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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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 less than 30ms) - AB Test results - Key Learnings - How can we improve further --- This session is sponsored by mobile.de