From Legacy Search to Vespa: What a Real PoC Taught Us
Session Abstract
For years, Germany’s largest classifieds website relied on a search-first relevance approach because structured data was sparse. This talk shares how we introduced Vespa in the Motors category, enriched signals with embeddings and extracted attributes, and migrated step by step; what worked, what failed, and which lessons only a real PoC reveals.
Session Description
For a long time, our homepage recommendations were driven by a search-first relevance approach. It was fast to iterate on and easy to reason about, but it limited personalization and proved fragile as soon as listings lacked structure or consistency.
In this talk, we describe how we transitioned to a Vespa-based recommendation stack, starting with the Motors category, where structured attributes are comparatively rich, and gradually expanding to less-structured categories. Rather than a big-bang rewrite, we incrementally replaced the legacy system.
We’ll share what the PoC taught us in practice: how we ran old and new systems in parallel, defined guardrails for quality and stability, and progressively improved signals by introducing text embeddings for listings and searches, extracting attributes from free text, and incorporating signals derived from images. We’ll also cover what didn’t work as expected, which assumptions broke under real traffic, and how evaluation and rollout influenced the final architecture.
Attendees will leave with concrete lessons on migrating relevance systems in production, running PoCs that expose real constraints, and introducing modern retrieval and ranking approaches when your data foundations are anything but perfect.
