Cold Start Solutions for Rich Media Content in Recommendation Systems

This article discusses the significance of cold start for rich media content in recommendation systems. Rich media content, including short videos and live streams, requires special attention to cold start due to its low production barriers, short production cycles, and high freshness requirements from consumers. The article presents methods to detect cold start problems, including calibration analysis and content lifecycle curve monitoring.

The article then explores solutions to cold start problems, focusing on two key techniques: the rise channel and cross-modal content understanding. The rise channel is a multi-stage selection process that helps promote quality content to become viral hits. The article provides practical experience from NetEase Cloud Music's implementation, including the transition from early personalization to later "breaking the circle" and prioritizing new content distribution to active users.

Cross-modal content understanding is presented as crucial for improving the efficiency of traffic utilization. The article explains information extraction and integration with downstream recommendation systems, covering techniques like CLIP for unified representation of images and text, CB2CF for vector fitting, and dual-tower models for user preference prediction. The article concludes with NetEase Cloud Music's practical results, including 25% improvement in new content distribution efficiency, 20%+ increase in click-through rate, and 40% reduction in review manpower through cross-modal technology.