Building an Intelligent News Recommendation System Inside Sohu News App

Sohu, a NASDAQ-listed Chinese online media company, has built an intelligent news recommendation system inside its news app using semantic vector search. The system uses user profiles built from browsing history to fine-tune personalized content recommendations over time, improving user experience and engagement. It leverages Milvus, an open-source vector database built by Zilliz, to process massive datasets efficiently and accurately, reducing memory usage during search and supporting high-performance deployments. The recommendation system relies on the Deep Structured Semantic Model (DSSM), which uses two neural networks to represent user queries and news articles as vectors. It also utilizes BERT-as-service for encoding news articles into semantic vectors, extracting semantically similar tags from user profiles, and identifying misclassified short text. The use of Milvus has significantly improved the real-time performance of Sohu's news recommendation system and increased efficiency in identifying misclassified short text.

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