Collected and analyzed data on how users interact with news and built evaluation metrics to track user behaviors, identifying product growth points from user behavior to help improve the product's operational ROI by 20%.
Conducted comprehensive market research to identify distinct user demographics within the NetEase News audience. Segment the user base into defined groups based on browsing habits, content preferences, and engagement metrics.
Collaborated with the algorithm engineering team to refine the content recommendation algorithm in C++. Implemented decision tree models for efficient content categorization and filters, which improved personalized news delivery and increased CTR by 30%.