Case Study: Sohu achieves 10x faster personalized news recommendations with Milvus from Zilliz

Sohu Elevates Personalized News Recommendations with Milvus

Sohu, a NASDAQ-listed internet services company, faced significant challenges in powering its news recommendation system. Their previous vector search technology was slow, provided inaccurate recommendations, and consumed excessive memory. Sohu also struggled with the precise classification of short-text news articles, which was crucial for delivering relevant content to users.

To address this, Sohu implemented the Milvus vector database from Zilliz to build a new, high-performance search engine. The solution used semantic vectors to power recommendations and a novel approach to improve news classification accuracy. The results were transformative: Milvus delivered a 10x improvement in vector retrieval speed, reduced memory consumption, and increased Sohu's short-news classification accuracy to over 95%, leading to a more personalized and engaging user experience.

Sohu

Tingting Wang

NLP Algorithm Engineer