Vespa.ai redefines the real-time standard for recommender systems with an architecture that compresses feature retrieval and model inference into a 10-millisecond latency. The system's workflow consists of three key phases: rapid filtering of candidate sets through a backward index, loading of user behavioral and content features, and ultimately millisecond-level inference performed by integrated TensorFlow or ONNX models.Spotify's practice has shown that this architecture enables recommended content updates to be made from hourly to secondly, dramatically enhancing personalized experiences.
In terms of technical implementation, the platform adopts memory computing mode to avoid disk IO bottleneck, and works with dynamic slicing mechanism to realize horizontal expansion. The unique hierarchical ranking function supports the two-stage processing of coarse ranking and fine ranking, which ensures the effect and controls the cost. In the news recommendation scenario, the system can instantly reflect the user's click feedback; e-commerce platforms use this feature to realize real-time product recommendation of "look and see", and the conversion rate is more than 30% higher than that of batch processing mode.
This answer comes from the articleVespa.ai: an open source platform for building efficient AI search and recommendation systemsThe
