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Modern Recommendation Systems — Retrieval Ranking Architecture
Modern recommendation systems have evolved to adopt a retrieval-ranking architecture, which plays a crucial role in effectively and efficiently filtering and ranking relevant items. This architecture is designed to maximize the utility of recommendation systems in production.
The retrieval-ranking architecture consists of two main components: the retrieval component and the ranking component.
RETRIEVAL COMPONENT
The retrieval component plays a crucial role in the recommendation system by efficiently selecting a subset of items from a vast pool of candidates that are potentially relevant to a user’s preferences.
This process involves the utilization of various techniques, including collaborative filtering, content-based filtering, and hybrid approaches.
- Collaborative filtering is a widely used technique in recommendation systems. It analyzes user behavior and preferences to identify similar users or items. By examining the historical data of users, collaborative filtering can identify patterns and similarities in their preferences. This technique…
