Product vectors should be updated based on the rate of change in your product data and the impact on downstream systems. There’s no universal rule, but a common approach is to refresh them when product attributes, user interactions, or business requirements shift significantly. For example, an e-commerce platform adding new products daily might update vectors weekly, while a streaming service with real-time content trends might require daily updates. The goal is to balance freshness with computational cost—over-updating wastes resources, while under-updating risks stale recommendations or search results.
Several factors influence update frequency. First, consider how often product data changes. If prices, inventory, or descriptions update hourly (e.g., flash sales), vectors need frequent retraining. Second, monitor user behavior: if preferences shift rapidly (e.g., seasonal trends), vectors must adapt to reflect new patterns. Third, evaluate system performance—if metrics like click-through rates drop, it may signal outdated vectors. For instance, a travel booking site might update vectors daily during peak seasons to capture fluctuating hotel availability and pricing but switch to weekly updates in slower periods. Tools like data versioning and A/B testing can help validate update intervals without disrupting live systems.
Technical implementation also plays a role. Automated pipelines using tools like Airflow or Kubeflow can schedule updates efficiently. For example, a news aggregator might retrain vectors every 6 hours to reflect breaking stories, while a SaaS platform with stable features might do monthly batch updates. Consider incremental updates for small changes (e.g., price adjustments) and full retraining for structural shifts (e.g., new product categories). Always test updates in staging environments to avoid performance regressions. Monitoring data drift via metrics like cosine similarity between old and new vectors can also trigger on-demand updates, ensuring alignment with current data.