Metarank is an open-source ranking service designed to enhance search and recommendation systems. It enables developers to integrate customer signals, personalize results in real-time, and leverage large language models (LLMs) for semantic understanding. As an open-source alternative to platforms like AWS Personalize and Tecton, Metarank offers greater transparency, flexibility, and control over data and deployment.
Key features include:
- Semantic Neural Search: Utilizes state-of-the-art LLMs to improve search accuracy.
- Recommendations: Supports trending and similar-item recommendations using collaborative filtering.
- Personalization: Employs secondary reranking (LambdaMART) for personalized search results.
- AutoML: Offers automatic feature generation and model retraining.
- A/B Testing: Facilitates A/B testing through multiple model serving.
Use cases:
- E-commerce: Powering "you may also like" widgets and personalized product recommendations.
- Search Engines: Improving search result relevance by incorporating user behavior and semantic understanding.
- Content Discovery: Enhancing content recommendations based on user preferences and trending topics.
