Ads Recommendation System
Building a cascade of recommendation systems for ads is a common approach to handle large-scale ad recommendations efficiently. This approach typically involves multiple stages, each narrowing down the set of candidate ads while increasing in complexity and computational cost. Here’s how you can build such a cascade:
1. Retrieval Stage:
This stage uses efficient methods like HSNN to quickly narrow down millions of ads to thousands of candidates. It’s designed for speed and scalability.
2. Ranking Stage:
This stage takes the candidates from the retrieval stage and applies more complex models to rank them more accurately.
- Use more sophisticated features and models (e.g., gradient boosting, deep neural networks).
- Incorporate more user-item interaction features.
- Compute more precise relevance scores.
3. Re-ranking Stage:
This final stage fine-tunes the ranking, considering additional factors:
- Apply business rules and constraints.
- Consider diversity and novelty of recommendations.
- Incorporate real-time context or user feedback.
